ES2658921T3 - Multigenic prognosis test for lung cancer - Google Patents

Abstract

A method of providing a prognosis for non-small cell lung cancer (NSCLC) in a subject, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that specifically bind each member of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; and (b) determine a subject risk score based on the levels of nucleic acid expression of the biomarkers in the sample or determine whether the biomarkers are differentially expressed or not at the level of nucleic acid in the sample; and (c) provide a prognosis for NSCLC based on the subject's risk score or differential expression.

Description

Multigenic prognosis test for lung cancer

5 Reference to related requests

This application claims the priority benefit of U.S. Provisional Patent Application Serial No. 61 / 504,063, filed July 1, 2011, and U.S. Provisional Patent Application Serial No. 61 / 504,193, filed July 2, 2011.

Background of the invention

The probability of long-term survival for patients with lung cancer is poorly defined by the clinical phase and histopathological findings. Gene microarray identification has been published

15 identified as prognoses for lung cancer, although there is a need for a precise multigenic polymerase chain reaction (PCR) assay that can predict the risk of mortality among patients with lung cancer.

Brief summary of the invention

In one aspect, a method is provided to provide a prognosis for lung cancer in a subject, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that specifically bind a panel of biomarkers comprising BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3, and (b) determine whether markers are differentially expressed in

25 the sample or not; thus providing a prognosis for lung cancer.

In one embodiment, the reagent is a nucleic acid. In another embodiment, the reagent is an oligonucleotide. In another embodiment, the reagent is a set of PCR primers. In another embodiment, the reagent is an antibody.

In one embodiment, lung cancer is non-squamous lung cancer. In another embodiment, non-squamous lung cancer is stage I. In another embodiment, non-squamous lung cancer is stage II. In another embodiment, non-squamous lung cancer is stage III. In another embodiment, non-squamous lung cancer is stage IV.

In one embodiment, the sample is from lung tissue or a lung tumor biopsy.

In one embodiment, the forecast provides a risk assessment. In some embodiments, the risk assessment is based on 5-year mortality. In some embodiments, the risk assessment is a high, intermediate or low risk assessment for 5-year mortality.

In one aspect, a kit is provided, the kit comprising reagents that specifically bind to a panel of biomarkers comprising BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3. In one embodiment, the reagent is a set of reverse transcriptase.

In yet another aspect, a method of determining the prognosis of a subject having lung cancer is provided by measuring in a biological sample the methylation levels of a biomarker panel comprising BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3; wherein said biological sample is obtained from said subject and said levels of methylation are indicative of said prognosis.

In another aspect, a report is provided, the report comprising a prognosis of a subject having lung cancer, said prognosis having been determined by quantifying in a biological sample of the subject the expression levels of a panel of biomarkers comprising BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3; wherein said expression levels are indicative of said

55 forecast.

In another aspect, a method is provided for determining a treatment plan, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that each specifically bind a member of a panel of Biomarkers comprising BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3, (b) determine whether markers are differentially expressed in the sample or not, (c) provide a prognosis for lung cancer, (d) determine a risk assessment for 5-year mortality based on the prognosis for lung cancer and (c) devise a treatment plan based on risk assessment.

In another aspect, a method is provided to provide a prognosis for lung cancer in a subject, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that are

each specifically bind a member of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A, and (b) determine whether biomarkers are expressed in differential form in the sample or not; thus providing a prognosis for lung cancer. In some embodiments, the determination of whether biomarkers are differentially expressed in the

5 shows or does not additionally comprise normalizing the expression levels of the biomarkers with respect to constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any combination thereof. In certain embodiments, the expression levels of the biomarkers are normalized against the average CT value of the constituent genes. In one embodiment, BAG1, BRCA1, CDC6, CDK2AP1, FUT3, IL11 and RND3 indicate in the subject an increased probability of mortality and in which ERBB3, LCK, SH3BGR and WNT3A indicate in the subject a decreased probability of mortality.

In one embodiment, the reagents are nucleic acids. In another embodiment, the reagents are oligonucleotides. In another embodiment, the reagents are sets of PCR primers. In another embodiment, the reagents are antibodies.

15 In one embodiment, lung cancer is non-squamous lung cancer. In another embodiment, non-squamous lung cancer is stage I. In another embodiment, non-squamous lung cancer is stage II. In another embodiment, non-squamous lung cancer is stage III. In another embodiment, non-squamous lung cancer is stage IV.

In one embodiment, the sample is from a tumor surgically removed. In another embodiment, the sample is from lung tissue or a lung tumor biopsy.

In one embodiment, the forecast provides a risk assessment. In some embodiments, the risk assessment is based on 5-year mortality. In some embodiments, the risk assessment is an assessment

25 high, intermediate or low risk for 5-year mortality.

In another aspect, a kit is provided comprising reagents that each specifically bind a member of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A In certain embodiments, the reagents are reverse transcriptase sets. In some embodiments, the kit additionally comprises constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any combination thereof.

In another aspect, a method is provided to provide a prognosis of a subject having lung cancer, said method comprising measuring in a biological sample the methylation levels of a panel of

35 biomarkers consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; wherein said biological sample is obtained from said subject and said levels of methylation are indicative of said prognosis.

In yet another aspect, a report is provided that comprises a prognosis of a subject having lung cancer, said prognosis having been determined by quantifying in a biological sample of the subject the expression levels of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; wherein said expression levels are indicative of said prognosis. In some embodiments, the subject quantification of the expression levels of a biomarker panel in the biological sample further comprises normalizing the expression levels of the biomarkers with respect to

45 to constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any combination thereof. In certain embodiments, the biomarker expression levels are normalized against the average CT value of the constitutive genes.

In yet another aspect, a method is provided for determining a treatment plan, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that each specifically bind a member of a panel of biomarkers consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A, (b) determine whether the markers are differentially expressed in the sample or not, (c) provide a prognosis for lung cancer, (d) determine a risk assessment for 5-year mortality based on the prognosis for lung cancer, and (e) devise a treatment plan

55 based on risk assessment. In some embodiments, the determination of whether the biomarkers are expressed in the sample differentially or does not additionally comprise normalizing the expression levels of the biomarkers with respect to constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any of the combinations thereof. In certain embodiments, the biomarker expression levels are normalized against the average CT value of the constitutive genes.

In yet another aspect, a method is provided to provide a prognosis for lung cancer in a subject, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that each specifically bind to a member of a biomarker panel consisting of BAG 1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; (b) determine a subject risk score based on the levels of expression of the biomarkers in the sample; and (c) provide a prognosis for lung cancer based on the subject's risk score. In some embodiments, the

Determination of a subject risk score based on the levels of expression of the biomarkers in the sample further comprises normalizing the levels of expression of the biomarkers with respect to constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any of the combinations thereof. In certain embodiments, the expression levels of the biomarkers are normalized against the value

5 CT average of the constitutive genes.

Brief description of the figures

Figure 1 shows that lung cancer is the most common cause of cancer death and shows that for stage 1 cancers the prognosis for 5-year survival is approximately 60%.

Figure 2 shows prognostic genomic models.

Figure 3 is an example of a Kaplan-Meier survival analysis of low, intermediate and high risk patients, grouped by risk score using the algorithm of Example 1.

Figure 4 is a table summarizing the algorithm genes used in the algorithm of Example 2.

Figure 5A is a graph illustrating the probability of 5-year mortality by risk score, where the dashed lines are 95% confidence intervals (CI) and short lines perpendicular to the X axis are individual risk scores For each patient. Figure 5B is a graph illustrating the increase in the instantaneous risk ratio (RRI) of the global mortality at 5 years per subgroup for each gradual increase in the risk category (for example, low to intermediate and intermediate to high), where the square sizes are proportional to the group size and AJCC refers to American Joint

25 Committee on Cancer.

Figure 6A is a graph illustrating overall survival for the entire cohort. Figure 6B is a graph illustrating the specific survival for lung cancer for the entire cohort, in which deaths from non-lung cancer are censored. Figure 6C is a graph illustrating the overall survival for 330 patients with stage IA and IB disease of the American Joint Commission on Cancer, considered as low risk according to conventional pathological criteria (National Comprehensive Cancer Network).

Figure 7 shows graphs that represent (A) overall survival for the entire cohort and survival in patients with disease in (B) stage I, (C) in stage II and (D) in stage III.

Figure 8 is a block diagram illustrating an exemplary computer system, in accordance with various embodiments.

Figure 9 is a flow chart illustrating aspects of a method according to an embodiment of the disclosure.

Detailed description of the invention

45 Introduction

The invention presents the identification of expression profiles of certain groups of genes that allow the precise prognosis of mortality in early stage lung cancer. Ideally, a prognostic tool should provide accurate risk stratification, should be clinically feasible to use in everyday practice and should be economical. Such a trial would be of particular benefit for patients with non-squamous NSCLC in stage I or II surgically removed. The current usual treatment for most non-squamous NSCLC in stage I is lobulectomy and lymphadenectomy of mediastinal nodes, without adjuvant chemotherapy. A better identification of subsets of patients with a good prognosis could allow the use of minor surgical procedures with equivalent survival potential. Conversely, the

55 stage I subsets with a poor prognosis could be selected for treatment with adjuvant chemotherapy to reduce the risk of distant metastases using current usual treatment agents. In addition, patients identified with a poor prognosis could also be considered for inclusion in clinical trials that test novel strategies and new therapeutic agents. Given the current limitations of chemotherapy in stage I disease, a bioassay that is prognostic and predictive of the benefit of chemotherapy would be especially beneficial. Finally, it is likely that non-squamous NSCLC in stage I will be of increasing importance in the future. Although approximately 20-30% of patients currently diagnosed with non-squamous NSCLC are stage I, this proportion is likely to grow due to the recent advent of lung cancer screening by CT scan.

65 Currently, patients with stage II NSCLC are advised to undergo adjuvant chemotherapy after an attempt at curative excision. However, the documented benefit of chemotherapy for these patients

in terms of absolute improvement of 5-year survival is small. As a result, many patients give up chemotherapy, particularly as they recover from their attempted curative surgery. A bioassay that can better assign the risk of recurrence for stage II patients may therefore improve compliance with current usual recommendations for adjuvant therapy in patients who are

5 at higher risk of recurrence. In a controlled experimental context, therapy may even be discontinued in patients who are at the lowest risk of recurrence even in stage II.

In one embodiment described herein, a trial was developed based on the expression patterns of 426 patients who had undergone the removal of a stage I-IV squamous lung cancer (NSCLC) at the University of California, San Francisco. RNA was extracted from samples of FFIP tissues (fixed in formalin and included in paraffin) and expression levels were evaluated for 11 target genes related to patient prognosis. We sought a trial that tended to assign a higher average risk score to patients who had died than to those who had survived a 5-year follow-up period. Patients whose samples received the highest risk scores would be considered at increased risk of

15 die within a period of 5 years after the operation, while patients whose samples received a low score would be more likely to have survived during this time interval after their operations.

The prognostic trial was developed by correlating the expression patterns of BAG1, BRCA1, CDC6, CDK2AP1,

20 ERBB3, FUT3, IL11, LCK, RND3, SH3BGR, WNT3A, which are related to the patient's prognosis (in particular because the prognosis is related to the 5-year overall survival results) using the Cox proportional instantaneous risk model . The prognostic test provides a prognosis for lung cancer in a subject by determining in one sample the expression of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A. The selection of each of a biomarker panel that includes

BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A, offers a great improvement over the prior art. WO2008 / 151072 (The Regents of the University of California) describes methods for the prognosis of lung cancer patients that are based on the expression analysis of a set of biomarkers.

30 Next, a risk score is obtained for each patient by inserting the expression levels of each of the 11 prognostic genes into a risk scoring algorithm. The risk groups are also described in this document based on these risk scores, placing patients in different risk categories according to their risk score. For example, "Low Risk", "Intermediate Risk" or "High Risk".

Additionally, a multigenic diagnostic kit is described herein, composed of the markers described herein, which can be used to provide a prognosis for lung cancer patients, and a report comprising a prognosis of a subject. who has lung cancer by quantifying the levels of expression of the markers described herein.

Definitions

"Lung cancer" generally refers to two main types of lung cancer categorized by the size and appearance of malignant cells: non-small cell lung cancer (approximately 80% of cases) and microcytic (approximately 20). % of the cases). "Non-small cell lung cancer" (NSCLC) includes squamous carcinoma. Lung adenocarcinoma is the most common subtype of NSCLC and other subtypes of lung cancer include bronchioalveolar carcinoma, large cell carcinoma, carcinoid tumor, adenoid cystic carcinoma, cylindroma and mucoepidermoid carcinoma. In one embodiment, lung cancers are staged according to stages I-IV, with I being an early stage and IV being the most

50 advanced.

"Prognosis" refers, for example, to overall survival, long-term mortality and disease-free survival. In one embodiment, long-term mortality refers to death within 5 years after the diagnosis of lung cancer.

55 “Risk assessment” refers to the relative risk an individual faces with respect to mortality. For example, a prognosis that provides a high-risk assessment for 5-year mortality has a higher probability of mortality within 5 years than an individual who has a low-risk assessment for 5-year mortality. In one embodiment, the prognosis for long-term mortality is "high risk", for example,

60 high risk of mortality, "intermediate risk", for example, intermediate risk of mortality or "low risk", for example, low risk of mortality. The stage of the cancer and the prognosis can be used to adjust a therapy for the patient that provides a better outcome, for example, systemic therapy and surgery, only surgery or systemic therapy alone. The risk assessment can be divided as desired, for example, in the median, in tertiary groups, quaternary groups, etc.

Other forms of cancer include carcinomas, sarcomas, adenocarcinomas, lymphomas, leukemias, etc., including solid and lymphocytic cancers, head and neck cancer, for example, oral, pharyngeal or tongue cancer, kidney cancer , breast, kidney, bladder, colon, ovary, prostate, pancreas, stomach, brain, head and neck, skin, uterine, testis, esophagus and liver, including hepatocarcinoma, lymphoma, including lymphomas

5 non-Hodgkin (eg, Burkitt, small cell and large cell lymphomas) and Hodgkin lymphoma, leukemia and multiple myeloma.

The term "marker" refers to a molecule (usually a protein, nucleic acid, carbohydrate or lipid) that is expressed in the cell, expressed on the surface of a cancer cell or secreted by a cancer cell, compared to a non-cancerous cell, and which is useful for the diagnosis of cancer, to provide a prognosis and to preferentially direct a pharmacological agent towards the cancer cell. Often, such markers are molecules that are overexpressed in a lung cancer or other cancer cells, compared to a non-cancerous cell, for example, an overexpression of 1 time, an overexpression of 2 times, an overexpression of 3 times or more , compared to a normal cell.

Additionally, a marker can be a molecule that is inappropriately synthesized in the cancer cell, for example, a molecule that contains deletions, additions or mutations, compared to the molecule expressed in a normal cell. Alternatively, such biomarkers are molecules that are underexpressed in a cancer cell, as compared to a non-cancerous cell, for example, a 1-time subexpression, a 2-time subexpression, a 3-fold or more subexpression. Additionally, a marker can be a molecule that is inappropriately synthesized in a cancer, for example, a molecule that contains deletions, additions or mutations, compared to the molecule expressed in a normal cell.

The person skilled in the art will understand that the markers can be used in combination with other markers or tests for any of the uses, for example, the prediction, diagnosis or prognosis of the cancer, disclosed

25 in this document.

"Biological sample" includes tissue cuts such as biopsy and autopsy samples, and frozen cuts taken for histological purposes. Such samples include blood and hematological fractions or products (for example, serum, platelets, red blood cells and the like), sputum, bronchoalveolar lavage, cultured cells, for example, primary cultures, explants and transformed cells, feces, urine, etc. A biological sample is usually obtained from a eukaryotic organism, more preferably a mammal such as a primate, for example, a chimpanzee or a human being; cow; dog; cat; a rodent, for example, guinea pig, rat, mouse; rabbit or a bird; reptile or fish

A "biopsy" refers to the procedure of extracting a tissue sample for diagnostic or prognostic evaluation, and to the test tissue sample itself. Any known biopsy technique can be applied in the art for the diagnostic and prognostic methods of the present invention. The biopsy technique applied will depend on the type of tissue to be evaluated (for example, lung, etc.), the size and type of the tumor, among other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, incision biopsy, puncture biopsy, surgical biopsy and bone marrow biopsy. A "excisional biopsy" refers to the removal of the entire tumor mass with a small margin of normal tissue surrounding it. An "incision biopsy" refers to the removal of wedge tissue from within the tumor. A diagnosis or prognosis made by endoscopic or radiographic guidance requires "biopsy by thick needle puncture" or a "fine needle biopsy," which generally results in a suspension of cells from within a target tissue. The

45 biopsy techniques are discussed, for example, in Harrison's Principles of Internal Medicine, Kasper, et al., Eds., 16th ed., 2005, Chapter 70 and throughout Part V.

The terms "overexpress", "overexpression" or "overexpressed" refer interchangeably to a protein or nucleic acid (RNA) that is translated or transcribed at a detectably higher level, usually in a cancer cell, compared to a normal cell . The terms include overexpression due to transcription, posttranscriptional processing, translation, posttranslational processing, cell localization (eg, organelle, cytoplasm, nucleus, cell surface) and the stability of RNA and proteins, compared to a normal cell Overexpression can be detected using conventional techniques for the detection of mRNA (i.e. RT-PCR, PCR, hybridization) or proteins (i.e., ELISA, techniques

55 immunohistochemicals). Overexpression can be 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more compared to a normal cell. In certain cases, overexpression is 1 time, 2 times, 3 times, 4 times or more levels of transcription or higher translation, compared to a normal cell.

The terms "subexpress", "subexpression" or "underexpressed", or "negatively regulated" refer interchangeably to a protein or nucleic acid that is translated or transcribed in a cancer cell at a detectably lower level, compared to a cell normal. The term includes subexpression due to transcription, post-transcriptional processing, translation, post-translational processing, cell localization (eg, organelle, cytoplasm, nucleus, cell surface) and the stability of RNA and proteins, compared to a control. Underexpression can be detected using conventional techniques for the detection of mRNA (i.e. RT-PCR, PCR, hybridization) or proteins (i.e., ELISA, immunohistochemical techniques). The underexpression can be 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or less, compared to

a control. In certain cases, subexpression is 1 time, 2 times, 3 times, 4 times or more levels of transcription or lower translation, compared to a control.

The term "differentially expressed" or "differentially regulated" generally refers to a protein

5 or nucleic acid that is overexpressed (positively regulated) or underexpressed (negatively regulated) in a sample, compared to at least one other sample, generally a cancer patient, compared to a tissue sample non-cancerous in the context of the present invention.

"Therapeutic treatment" and "cancer therapies" refer to chemotherapy, hormonal therapy, radiotherapy, immunotherapy and biological (targeted) therapy.

By "therapeutically effective amount or dose" or "sufficient amount or dose" is meant a dose that produces the effects for which it is administered. The exact dose will depend on the end of the treatment and will be determined by one skilled in the art using known techniques (see, for example, Lieberman, Pharmaceutical Dosage Forms (vols. 1

15 3, 1992); Lloyd, The Art, Science and Technology of Pharmaceutical Compounding (1999); Pickar, Dosage Calculations (1999) and Remington: The Science and Practice of Pharmacy, 20th Edition, 2003, Gennaro, Ed., Lippincott, Williams & Wilkins).

"Nucleic acid" refers to deoxyribonucleotides or ribonucleotides, and polymers thereof, in single or double stranded form and their complementary ones. The term encompasses nucleic acids containing known nucleotide analogs, or modified skeletal moieties or moieties, that are synthetic, of natural or non-natural origin, that have binding properties similar to the reference nucleic acid and that are metabolized in a manner similar to the reference nucleotides. Examples of such analogs include, without limitation, phosphorothioates, phosphoramidates, methyl phosphonates, chiral methylphosphonates, 2-O-methyl ribonucleotides, peptide nucleic acids (the

25 ANP).

Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (eg, degenerate codon substitutions) and complementary sequences, as well as the explicitly indicated sequence. Specifically, degenerate codon substitutions can be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with residues of mixed bases and / or deoxyinosine (Batzer et al., Nucleic Acid Res. 19: 5081 (1991); Ohtsuka et al., J. Biol. Chem. 260: 2605-2608 (1985), Rossolini et al., Mol. Cell. Probes

8: 91-98 (1994)). The term nucleic acid is used interchangeably with a gene, cDNA, mRNA, oligonucleotide and polynucleotide.

A particular nucleic acid sequence also implicitly encompasses "splice variants" and nucleic acid sequences encoding truncated forms of a protein. Similarly, a particular protein encoded by a nucleic acid implicitly encompasses any protein encoded by a splice variant, or a truncated form of a nucleic acid. "Variants of splicing", as the name suggests, are products of the alternative splicing of a gene. After transcription, an initial nucleic acid transcript can be cut and spliced so that the different (or alternate) splicing products of the nucleic acid encode different polypeptides. The mechanisms for the production of splice variants vary, but include the alternate splicing of exons. Alternate polypeptides obtained from the same nucleic acid by transcription beyond the terminator codon are also encompassed in this

45 definition. Any of the products of a splicing reaction, including recombinant forms of splicing products, are included in this definition. Nucleic acids can be truncated at the 5 ’end or at the 3’ end. The polypeptides may be truncated at the N-terminus or the C-terminus. Truncated versions of nucleic acid or polypeptide sequences can be of natural origin or created recombinantly.

The terms "polypeptide", "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one or more amino acid residues are an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers.

55 The term "amino acid" refers to naturally occurring or synthetic amino acids, as well as amino acid analogues

or amino acid mimetics that act in a manner similar to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as amino acids that are subsequently modified, for example, hydroxyproline, γ-carboxyglutamate and O-phosphoserine. Amino acid analogs refers to compounds that have the same basic chemical structure as the naturally occurring amino acid, that is, a carbon that is attached to a hydrogen, a carboxyl group, an amino group and an R group, for example, homoserine, norleucine, methionine sulfoxide, methyl methionine sulphonium. Such analogs have modified R groups (eg, norleucine) or modified peptide skeletons, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimetics refers to chemical compounds that have a

A structure that is different from the general chemical structure of an amino acid, but acts in a manner similar to a naturally occurring amino acid.

The amino acids may be referred to herein by their commonly known three-letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Likewise, nucleotides can be named by their commonly accepted single letter codes.

5 "Conservatively modified variants" applies to the amino acid and nucleic acid sequences. With respect to particular nucleic acid sequences, conservatively modified variants refers to nucleic acids encoding identical or essentially identical amino acid sequences or, when the nucleic acid does not encode an amino acid sequence, for essentially identical sequences. Due to the degeneracy of the genetic code, a large number of functionally identical nucleic acids encode a given protein. For example, all codons GCA, GCC, GCG and GCU encode the amino acid alanine. Therefore, at each position where an alanine is specified by a codon, the codon can be modified to any of the corresponding codons described without altering the encoded polypeptide. Such variations of nucleic acid are "silent variations", which are a kind of conservatively modified variations. Here, each nucleic acid sequence encoding a polypeptide

15 also describes each possible silent variation of the nucleic acid. One skilled in the art will recognize that each codon in a nucleic acid (except AUG, which is usually the only codon for methionine, and TGG, which is usually the only codon for tryptophan) can be modified to produce a functionally identical molecule. Therefore, each silent variation of a nucleic acid encoding a polypeptide is implicit in each sequence described, with respect to the expression product but not with respect to the actual probe sequences.

As for amino acid sequences, one skilled in the art will recognize that individual substitutions, deletions or additions in a nucleic acid, peptide, polypeptide or protein sequence, which modifies, adds or deletes an individual amino acid or a small percentage of amino acids in the encoded sequence is a

"Conservatively modified variant" wherein the modification results in the substitution of an amino acid with a similar amino acid from the chemical point of view. Tables of conservative substitutions that provide similar amino acids from a functional point of view are well known in the art. Such conservatively modified variants add to, and do not exclude, polymorphic variants, interspecies homologues and alleles of the invention.

The following eight groups each contain amino acids that are conservative substitutions for each other: 1) Alanine (A), Glycine (G); 2) Aspartic acid (D), Glutamic acid (E); 3) Asparagine (N), Glutamine (Q); 4) Arginine (R), Lysine (K); 5) Isoleucine (I), Leucine (L), Methionine (M), Valine (V); 6) Phenylalanine (F), Tyrosine (Y), Tryptophan (W); 7) Serine (S), Threonine (T) and 8) Cysteine (C), Methionine (M). See, for example, Creighton, Proteins

35 (1984).

A "marker" or a "detectable fraction" is a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, chemical or other physical means. For example, useful markers include 32P, fluorescent dyes, electrodensactive reagents, enzymes (for example, as commonly used in an ELISA), biotin, digoxigenin or haptens, and proteins that can be manufactured to be detectable, for example, by incorporating a radiolabel in the peptide, or used to detect antibodies specifically reactive with the peptide.

The term "recombinant" when used in reference, for example, to a cell or to a nucleic acid, protein or

Vector, indicates that the cell, nucleic acid, protein or vector, has been modified by the introduction of a heterologous nucleic acid or protein, or by the alteration of a native nucleic acid or protein, or that the cell is obtained from a cell so modified. Therefore, for example, recombinant cells express genes that are not within the native (non-recombinant) form of the cell or express native genes that otherwise express themselves abnormally, are underexpressed or not expressed absolutely.

The phrase "stringent hybridization conditions" refers to conditions in which a probe will hybridize with its target sub-sequence, usually in a complex mixture of nucleic acids, but not with other sequences. Rigorous conditions are sequence dependent and will be different in different circumstances. Longer sequences specifically hybridize at higher temperatures. An extensive guide to hybridization of nucleic acids is found in Tijssen, Techniques in Biochemistry and Molecular Biology - Hybridization with Nucleic Probes, “Overview of principles of hybridization and the strategy of nucleic acid assays” (1993). In general, the stringent conditions are selected to be approximately 5-10 ° C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. Tm is the temperature (at a defined ionic strength, pH and nucleic acid concentration) at which 50% of the complementary probes with the target hybridize with the target sequence in equilibrium (since the target sequences are present in excess , at Tm, in the balance 50% of the probes are occupied). Rigorous conditions can also be achieved with the addition of destabilizing agents such as formamide. For selective or specific hybridization, a positive signal is at least twice the background, preferably 10 times the background hybridization. Exemplary rigorous hybridization conditions may be as follows: 50% formamide, 5x SSC and 1% SDS, incubating at

65 42 ° C, or 5x SSC, 1% SDS, incubating at 65 ° C, with a wash in 0.2x SSC and 0.1% SDS at 65 ° C.

Nucleic acids that do not hybridize to each other under stringent conditions are still substantially identical if the polypeptides they encode are substantially identical. This occurs, for example, when a copy of a nucleic acid is created using the maximum codon degeneracy allowed by the genetic code. In such cases, nucleic acids normally hybridize under moderately stringent hybridization conditions. The

Exemplary "moderately stringent hybridization conditions" include hybridization in a 40% formamide buffer, 1M NaCl, 1% SDS at 37 ° C and a wash in 1X SSC at 45 ° C. A positive hybridization is at least twice the background. Those skilled in the art will readily recognize that alternative hybridization and washing conditions can be used to provide conditions of similar stringency. Additional guidelines for determining hybridization parameters are provided in numerous references, for example, in Current Protocols in Molecular Biology, ed. Ausubel, et al., Cited above.

For PCR, a temperature of approximately 36 ° C is typical for amplification with low stringency, although the mating temperatures may vary between approximately 32 ° C and 48 ° C, depending on the length of the primer. For a PCR amplification of high stringency, a temperature of

15 about 62 ° C, although high stringent mating temperatures may vary from about 50 ° C to about 65 ° C, depending on the length and specificity of the primer. Typical cycle conditions, for both high and low stringency amplifications, include a denaturation phase of 90 ºC-95 ºC for 30 s -2 min, a mating phase that lasts 30 s -2 min and an extension phase of about 72 ° C for 1-2 min. Protocols and guidelines for amplification reactions of low and high stringency are provided, for example, in Innis et al. (1990) PCR Protocols, A Guide to Methods and Applications, Academic Press, Inc. N. Y.

"Antibody" refers to a polypeptide comprising a conserved framework region from an immunoglobulin gene or fragments thereof, which specifically binds and recognizes an antigen. The genes of

Immunoglobulins identified include the genes from the constant region kappa, lambda, alpha, gamma, delta, epsilon and mu, as well as the large number of genes from the variable region of immunoglobulin. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta or epsilon, which in turn define the classes of immunoglobulin, IgG, IgM, IgA, IgD and IgE, respectively. Normally, the antigen binding region of an antibody will be the most critical in binding specificity and affinity. The antibodies can be polyclonal or monoclonal, obtained from serum, a hybridoma or can be cloned recombinantly, and they can also be chimeric, primatized or humanized.

An exemplary immunoglobulin (antibody) structural unit comprises a tetramer. Each tetramer is composed of two identical pairs of polypeptide chains, each pair having a "light" chain

35 (approximately 25 kDa) and a "heavy" (approximately 50-70 kDa). The N-terminus of each chain defines a variable region of approximately 100 to 110, or more, amino acids primarily responsible for antigen recognition. The terms variable light chain (VL) and variable heavy chain (VH) refer to these light and heavy chains respectively.

Antibodies exist, for example, as intact immunoglobulins or as a series of well characterized fragments produced by digestion with various peptidases. Therefore, for example, pepsin digests an antibody below the disulfide bonds in the hinge region to produce F (ab) '2, a Fab dimer that is itself a light chain attached to VH-CH1 by a bond disulfide F (ab) ’2 can be reduced under mild conditions to break the disulfide bond in the hinge region, thus converting the F (ab)’ 2 dimer into a

45 monomer Fab ’. The Fab 'monomer is essentially the Fab with part of the hinge region (see Fundamental Immunology (Paul Ed., 3rd ed., 1993). Although several antibody fragments are defined in terms of the digestion of an intact antibody, an expert in the matter will appreciate that such fragments can be synthesized de novo either chemically or using the recombinant DNA methodology.Therefore, the term antibody, as used herein, also includes antibody fragments produced by the modification of complete antibodies or synthesized de novo using recombinant DNA methodologies (eg, single chain Fv) or those identified using phage display libraries (see, for example, McCafferty et al., Nature 348: 552-554 (1990) ).

In one embodiment, the antibody is conjugated to an "effector" fraction. The effector fraction can be a number

A variety of molecules, including labeling fractions such as radioactive markers or fluorescent markers, or may be a therapeutic fraction. In one aspect, the antibody modulates the activity of the protein.

The nucleic acids of the differentially expressed genes of the present invention or the polypeptides encoding, refers to all forms of nucleic acids (eg, pre-mRNA, mRNA) or proteins, their polymorphic variants, alleles, mutants and interspecies homologs that (as appropriate to nucleic acid or protein): (1) have an amino acid sequence that has more than about 60% amino acid sequence identity, 65%, 70%, 75%, 80%, 85 %, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% or more amino acid sequence identity, preferably along a region of at less about 25, 50, 100, 200, 500, 1000 or more amino acids, with a polypeptide encoded by a nucleic acid referred to or with an amino acid sequence described herein; (2) specifically bind to antibodies, for example polyclonal antibodies, generated against

an immunogen comprising an amino acid sequence referred to, immunogenic fragments thereof and conservatively modified variants thereof; (3) specifically hybridize under stringent conditions of hybridization with a nucleic acid encoding an amino acid sequence referred to and with the conservatively modified variants thereof; (4) they have a nucleic acid sequence having more than about 95%, preferably more than about 96%, 97%, 98%, 99% or more nucleotide sequence identity, preferably along a region of at least about 25, 50, 100, 200, 500, 1000 or more nucleotides, with a nucleic acid sequence referred to. A polynucleotide or polypeptide sequence normally comes from a mammal, including, but not limited to, a primate, for example, a human being; rodent, for

10 example, rat, mouse, hamster; cow, pig, horse, sheep or any mammal. The nucleic acids and proteins of the invention include both naturally occurring and recombinant molecules. Truncated and cut and spliced forms of these antigens are included in the definition.

The phrase "that binds specifically (or selectively)", when referring to a protein, acidic

Nucleic, antibody or small molecule compound, refers to a binding reaction that is determinative of the presence of the protein or nucleic acid, such as the differentially expressed genes of the present invention, often in a heterogeneous population of proteins or nucleic acids, or other biological products. In the case of the antibodies, under the designated immunoassay conditions, a specified antibody can bind to a particular protein at least twice the background and, more typically, more than 10 to 100 times the background.

The specific binding to an antibody under such conditions requires an antibody that is selected for its specificity for a particular protein. For example, polyclonal antibodies can be selected to obtain only polyclonal antibodies that are specifically immunoreactive with the selected antigen and not with other proteins. This selection can be achieved by subtracting antibodies that cross-react with other molecules. A variety of immunoassay formats can be used to

25 select immunoreactive antibodies specifically with a particular protein. For example, solid-phase ELISA immunoassays are routinely used to select immunoreactive antibodies specifically with a protein (see, for example, Harlow & Lane, Antibodies, A Laboratory Manual (1988) for a description of the formats of immunoassay and conditions that can be used to determine specific immunoreactivity).

The phrase "functional effects", in the context of tests to analyze compounds that modulate a marker protein, includes the determination of a parameter that is indirectly or directly under the influence of a biomarker of the invention, for example, one chemical or phenotypic Therefore, a functional effect includes ligand binding activity, transcriptional activation or repression, the ability to proliferate cells, the

35 ability to migrate, among others. "Functional effects" includes in vitro, in vivo and ex vivo activities.

By "determining functional effects" is meant to test a compound that increases or decreases a parameter that is indirectly or directly under the influence of a biomarker of the invention, for example, measuring physical and chemical or phenotypic effects. Such functional effects can be measured by any means known to those skilled in the art, for example, changes in spectroscopic characteristics (eg, fluorescence, absorbance, refractive index); hydrodynamic properties (eg shape), chromatographic

or of protein solubility; ligand binding assays, for example, antibody binding; measurement of inducible markers or transcriptional activation of the marker; measurement of changes in enzyme activity; the ability to increase or decrease cell proliferation, apoptosis, cell cycle arrest, measurement of changes in cell surface markers. Functional effects can be evaluated by many means known to those skilled in the art, for example, microscopy for quantitative and qualitative measures of changes in morphological characteristics, measurement of changes in RNA or protein levels for other genes expressed in placental tissue, measurement of RNA stability, identification of the expression of a downstream gene or indicator (CAT, luciferase, β-gal, GFP and the like), for example, through

50 chemiluminescence, fluorescence, colorimetric reactions, antibody binding, inducible markers, etc.

"Inhibitors", "activators" and "modulators" of the markers are used to refer to activating, inhibiting or modulating molecules identified using in vitro and in vivo assays of biomarkers for cancer. Inhibitors are compounds that, for example, bind, block activity partially or totally, decrease, prevent, delay activation, inactivate, desensitize or negatively regulate the activity or expression of biomarkers for cancer. "Activators" are compounds that increase, open, activate, facilitate, enhance activation, sensitize, agonize or positively regulate the activity of biomarkers for cancer, for example, agonists. Inhibitors, activators or modulators also include genetically modified versions of biomarkers for cancer, for example, versions with modified activity, as well as ligands

60 of natural and synthetic origin, antagonists, agonists, antibodies, peptides, cyclic peptides, nucleic acids, antisense molecules, ribozymes, iRNA and siRNA molecules, small organic molecules and the like. Such assays for inhibitors and activators include, for example, the expression of biomarkers for cancer in vitro, in cells or cell extracts, the application of so-called modulating compounds and, then, the determination of functional effects on activity, as described. previously.

Samples or assays comprising biomarkers for cancer that are treated with a possible activator, inhibitor or modulator are compared with control samples without the inhibitor, activator or modulator to examine the degree of inhibition. Control samples (not treated with inhibitors) are assigned a relative value of 100% protein activity. The inhibition of biomarkers for cancer is achieved when the value of

The activity with respect to the control is approximately 80%, preferably 50%, more preferably 25-0%. The activation of biomarkers for cancer is achieved when the activity value with respect to control (not treated with activators) is 110%, more preferably 150%, more preferably 200-500% (i.e., two to five times higher with respect to the control), more preferably 1000-3000% higher.

The term "test compound" or "drug candidate," or "modulator," or grammatical equivalents as used herein, describes any molecule, whether of natural or synthetic origin, for example, a protein, oligopeptide (for example, about 5 to about 25 amino acids in length, preferably about 10 to 20 or 12 to 18 amino acids in length, preferably 12, 15 or 18 amino acids in length), small organic molecule, polysaccharide, peptide, circular peptide, lipid fatty acid

15 siRNA, polynucleotide, oligonucleotide, etc., to be tested for the ability to directly or indirectly modulate biomarkers for cancer. The test compound may be in the form of a library of test compounds, such as a combinatorial or randomized library that provides a sufficient variety of diversity. Test compounds are optionally linked to a fusion partner, for example, targeting compounds, rescue compounds, dimerization compounds, stabilizing compounds, addressable compounds and other functional fractions. Conventionally, new chemical entities with useful properties are generated by identifying a test compound (called "starting compound") with some suitable property or activity, for example, inhibitory activity, creating variants of the starting compound, and evaluating the property and activity of the variant compounds. High performance selection (SAR) methods are often used for such analysis.

A "small organic molecule" refers to an organic molecule, whether of natural or synthetic origin, having a molecular weight of more than about 50 daltons and less than about 2,500 daltons, preferably less than about 2,000 daltons, preferably between about 100 and about 1000 daltons, more preferably between about 200 and about 500 daltons.

Forecast Methods

The present invention provides methods for predicting or providing a prognosis for lung cancer by detecting the expression of a panel of differentially expressed markers in cancer. The panel includes

The genes encoding BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A. Prediction and prognosis involve determining the level of a biomarker polynucleotide panel for lung cancer or the corresponding polypeptides in a patient or patient sample, and then comparing the level with a reference value or interval. Normally, the reference value is representative of the levels of the polynucleotide or nucleic acid in a healthy person who does not suffer from, or is destined to develop, lung cancer, measured using a biological sample such as a lung biopsy or a sample of a body fluid The variation of the levels of a polynucleotide or the corresponding polypeptides of the invention with respect to the reference range (either up or down), indicates that the patient has an increased risk of long-term mortality.

The algorithm used to calculate a risk assessment score in a method disclosed herein can group the values of the level of gene expression, and the risk score can be obtained from any algorithm known in the art. The examples provided herein use exemplary algorithms that can be used to develop a risk assessment. The algorithms are sets of rules to describe the assessment of lung cancer risk using the expression of the gene panel described in this document. The set of rules can be defined exclusively in an algebraic way but can also include alternative or multiple decision points that require specific knowledge of the field, the interpretation of experts or other clinical indicators. Many algorithms can be developed that can provide different risk assessments using expression profiles from the gene panel described herein. For example, an individual's risk scores may

55 generated using the Cox proportional instantaneous risk model. An individual prognostic categorization can also be determined using a statistical model or machine learning algorithm, which calculates the probability of recurrence based on the individual's gene expression profiles.

Based on the determination of a risk, individuals can be divided into risk groups (for example, tertiles

or quartiles) based on a selected value of the risk score, where all individuals with values in a given range can be classified as belonging to a particular risk group. Therefore, the values chosen will define risk groups of patients with, respectively, greater or lesser risk. Risk groups can be further classified into different mortality intervals, for example, in a 6-month, 1-year, 2-year, 3-year, 4-year, 5-year, 10-year, 25-year-old mortality . Risk groups

65 can be further classified into different intervals of events associated with lung cancer, which may include, but are not limited to, probability of metastasis, recurrence, etc.

Various technological strategies for the determination of the expression levels of the gene panel, including, but not limited to, RT-PCR, microarrays, high performance sequencing, serial analysis of gene expression (SAGE, acronym) are presented herein. of serial analysis of gene expression) and Digital Gene Expression (DGE). The expression level of each gene can

5 be determined in relation to various characteristics of gene expression products, including exons, introns, protein epitopes and protein activity.

In a preferred embodiment, real-time PCR or quantitative reverse transcription (RTPCR) is used to examine the expression of the eleven panel markers, using RNA from a biological sample, such as a tumor tissue. No microdissection is necessary. RNA extraction can be performed by any method known to those skilled in the art, for example, methods that involve tissue digestion with proteinase K and precipitation of alcohol-based nucleic acids, treatment with DNase to digest the contaminating DNA, RNA purification using silica gel membrane technology, methods using commercially available kits such as Trizol and RNeasy, or any combination thereof. RT-PCR

15 in real time can be performed by any method known to those skilled in the art, for example, Taqman real-time PCR using Applied Biosystem assays. Gene expression is calculated with respect to clustered normal lung RNA and expression is normalized with respect to constitutive genes. Those skilled in the art select suitable oligonucleotide primers. In one embodiment, the assay is used for stage I, stage II, stage III or stage IV cancers. In one embodiment, the tissue sample comes from a tumor surgically removed.

In one embodiment, RNA biomarkers are examined using nucleic acid binding molecules such as probes, oligonucleotides, oligonucleotide matrices and primers, which detect differential expression of RNA in patient samples. In one embodiment, RT-PCR is used according to known conventional methods.

25 in the art. In another embodiment, quantitative RT-PCR assays such as those using available Taqman® assays of, for example, Applied Biosystems, can be used to detect nucleic acids and variants thereof. In other embodiments, micromatrices of nucleic acids can be used to detect nucleic acids. Also included within the scope of the present invention is the analysis of nucleic acids can be achieved using routine techniques such as Northern analysis or any other method based on hybridization with a nucleic acid sequence that is complementary to a portion of the coding sequence. of the marker (for example, hybridization by slot transfer). Reagents that bind to selected nucleic acid biomarkers can be prepared according to methods known to the person skilled in the art or can be purchased commercially.

35 Applicable PCR amplification techniques are described in, for example, Ausubel et al. and Innis et al., cited above. General methods of nucleic acid hybridization are described in Anderson, "Nucleic Acid Hybridization", BIOS Scientific Publishers, 1999. Amplification or hybridization of a plurality of nucleic acid sequences (eg, genomic DNA, mRNA or cDNA) may also be made from the mRNA or cDNA sequence arranged in a microarray. Microarray methods are generally described in Hardiman, "Microarrays Methods and Applications: Nuts & Bolts", DNA Press, 2003; and Baldi et al., "DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling," Cambridge University Press, 2002.

The analysis of nucleic acid markers can be performed using techniques known in the art including, but not limited to, sequence analysis and electrophoretic analysis. Non-limiting examples of sequence analysis include Maxam-Gilbert sequencing, Sanger sequencing, capillary DNA sequencing, thermal cycle sequencing (Sears et al., Biotechniques, 13: 626-633 (1992)) , solid phase sequencing (Zimmerman et al., Methods Mol. Cell Biol., 3: 39-42 (1992)), mass spectrometry sequencing such as desorption and laser ionization assisted by matrix-time-of-flight - mass spectrometry (MALDITOF / MS; Fu et al., Nat. Biotechnol., 16: 381-384 (1998)) and hybridization sequencing. Chee et al., Science,

274: 610-614 (1996); Drmanac et al., Science, 260: 1649-1652 (1993); Drmanac et al., Nat. Biotechnol., 16: 54-58 (1998). Non-limiting examples of electrophoretic analysis include flat gel electrophoresis, such as agarose or polyacrylamide gel electrophoresis, capillary electrophoresis and denaturing gradient gel electrophoresis.

In another aspect, antibody reagents can be used in assays to detect the expression levels of the protein biomarkers of the invention in patient samples, which use any of a series of immunoassays known to those skilled in the art. Immunoassay techniques and protocols are generally described in Price and Newman, "Principles and Practice of Immunoassay", 2nd edition, Grove's Dictionaries, 1997; and Gosling, "Immunoassays: A Practical Approach," Oxford University Press, 2000. A variety of immunoassay techniques can be used, including competitive and non-competitive immunoassays. See, for example, Self et al., Curr. Opin. Biotechnol., 7: 60-65 (1996). The term "immunoassay" encompasses techniques that include, but are not limited to, enzyme immunoassays (EIA) such as the enzyme-multiplied immunoassay technique (EMIT), the enzyme-linked immunosorbent assay (ELISA), the IgM antibody-capture ELISA (MAC ELISA). ) and the enzymatic microparticle immunoassay (MEIA); the

65 immunoassays with capillary electrophoresis (CEIA); radioimmunoassays (RIA); immunoradiometric assays (IRMA); fluorescent polarization immunoassays (FPIA) and chemiluminescence assays (CL). If desired, such

Immunoassays can be automated. Immunoassays can also be used in conjunction with laser-induced immunofluorescence. See, for example, Schmalzing et al., Electrophoresis, 18: 2184-93 (1997); Bao, J. Chromatogr. B. Biomed. Sci., 699: 463-80 (1997). Immunoassays with liposomes, such as liposome flow injection immunoassays and liposome immunosensors, are also suitable.

5 for use in the present invention. See, for example, Rongen et al., J. Immunol. Methods, 204: 105-133 (1997). In addition, nephelometry assays are suitable for use in the methods of the present invention, in which the formation of the protein / antibody complexes results in an increased light scattering that becomes a maximum frequency signal such as a function of marker concentration. Nephelometry assays are commercially available from Beckman Coulter (Brea, CA; Kit # 449430) and can be performed using a Behring nephelometer analyzer (Fink et al., J. Clin. Chem. Clin. Biochem., 27 : 261-276 (1989).

Expression levels of prognostic and / or predictive genes can be measured in a tumor tissue. For example, tumor tissue is obtained after surgical removal or excision of the tumor, or by tumor biopsy. The level of

Prognostic and / or predictive gene expression can also be measured in tumor cells recovered from a distant site of the tumor, for example circulating tumor cells or a body fluid.

In the assays described herein, a detectable fraction (direct or indirect detection) can be used. A wide variety of detectable fractions can be used, depending on the choice of the necessary sensitivity marker, the ease of conjugation with the antibody, the stability requirements and the available instrumentation and evacuation provisions. Suitable detectable fractions include, but are not limited to, radionuclides, fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate (FITC), Oregon Green ™, rhodamine, Texas red, tetrarrodamine isothiocyanate (TRITC), Cy3, Cy5, etc.) , fluorescent markers (eg, green fluorescent protein (GFP), phycoerythrin, etc.), fluorescent compounds

25 auto-activates that are activated by tumor-associated proteases, enzymes (eg, luciferase, horseradish peroxidase, alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin, metals and the like.

A chemiluminescent assay using a chemiluminescent antibody specific for nucleic acid is suitable for sensitive, non-radioactive detection of protein levels. Also suitable is a fluorochrome labeled antibody. Examples of fluorochromes include, but are not limited to, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas Red and lysamine. Indirect markers include various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), β-galactosidase, urease and the like. A horseradish peroxidase detection system can be used, for example, with the tetramethylbenzidine (TMB) chromogenic substrate, which produces a product

Soluble in the presence of hydrogen peroxide which is detectable at 450 nm. An alkaline phosphatase detection system with the chromogenic substrate p-nitrophenyl phosphate can be used, for example, which produces a soluble product easily detectable at 405 nm. Similarly, a βgalactosidase detection system can be used with the o-nitrophenyl-β-D-galactopyranoside chromogenic substrate (ONPG), which produces a soluble product detectable at 410 nm. A urease detection system with a substrate such as urea-bromocresol purple (Sigma Immunochemicals, St. Louis, MO) can be used.

A signal from a direct or indirect marker can be analyzed, for example, using a spectrophotometer to detect color of a chromogenic substrate; a radiation meter to detect radiation such as a gamma counter for the detection of 125I or a fluorometer to detect fluorescence in the presence of

45 light of a certain wavelength. For the detection of enzyme-bound antibodies a quantitative analysis can be done using a spectrophotometer such as an EMAX microplate reader (Molecular Devices; Menlo Park, CA) in accordance with the manufacturer's instructions. If desired, the assays of the present invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

The antibodies can be immobilized on a variety of solid supports, such as particles of a magnetic or chromatographic matrix, the surface of a test plate (for example, microtiter wells), pieces of a solid substrate material or membrane (for example, plastic, nylon, paper) and the like. A test strip can be prepared by coating the antibody or a plurality of antibodies in a matrix on a solid support. This strip

55 can then be immersed in the test sample and quickly processed through washing and detection steps, to generate a measurable signal, such as a colored spot.

Useful physical formats comprise surfaces that have a plurality of discrete and evaluable locations for the detection of a plurality of different markers. Such formats include microarrays and certain capillary devices. See, for example, Ng et al., J. Cell Mol. Med., 6: 329-340 (2002); U.S. Patent No. 6,019,944. In these embodiments, each discrete surface site may comprise antibodies to immobilize one or more markers for detection at each site. The surfaces may, alternatively, comprise one or more discrete particles (eg, microparticles or nanoparticles) immobilized at discrete locations of a surface, where the microparticles

65 comprise antibodies to immobilize one or more markers for detection.

The analysis can be carried out in a variety of physical formats. For example, the use of microtiter or automation plates could be used to facilitate the processing of a large number of test samples. Alternatively, single sample formats could be developed to facilitate diagnosis or prognosis at the appropriate time.

Alternatively, the antibodies or nucleic acid probes of the invention can be applied to biopsy sections of patients immobilized on microscope slides. Antibody staining or the resulting in situ hybridization pattern can be visualized using any one of a variety of microscopic fluorescent methods known in the art.

In another format, the various markers of the invention also provide reagents for in vivo imaging such as, for example, imaging of labeled reagents that detect the nucleic acids or encoded proteins of the biomarkers of the invention. For imaging purposes in vivo, reagents that detect the presence of proteins encoded by biomarkers for cancer, such as

15 antibodies can be labeled using an appropriate label, such as a fluorescent label.

The 11 gene panel described herein (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A) provides a more accurate prognostic test for lung cancer than other known gene combinations Currently in the art. The success of the different combinations of genes in the assignment of individuals to the high or low risk categories can be compared using the area under the curve of the analysis of the receiver's operational characteristic (AUROC, acronym for the area under the receiver operating characteristic) . The AUROC is a summative statistic that captures the ability of different models to accurately discriminate between risk groups (in this case, the risk of death within 5 years of surgical removal of a lung cancer tumor).

The AUROC analysis can be performed using various computer programs known in the art, including, but not limited to, the statistical computing software package R and the STATA computer software package. One skilled in the art will recognize that different packages of computer software can produce different statistics c. A larger AUROC c statistic indicates a more accurate prognostic gene signature.

Gene expression values contribute positively to a patient mortality risk (represented by an instantaneous risk ratio greater than 1.0) or negatively to a patient mortality risk (represented by a lower instantaneous risk ratio of 1.0). Therefore, each gene can either increase

35 the risk of mortality of a patient or decrease the risk of mortality of a patient. Risk genes are genes for which increased expression is associated with a higher risk of death, while protective genes are genes for which increased expression is associated with a lower risk of death. In some embodiments, BAG1, BRCA1, CDC6, CDK2AP1, FUT3, IL11 and RND3 of the panel of eleven genes are risk genes that indicate an increased probability of subject mortality, while ERBB3, LCK, SH3BGR and WNT3A are protective genes that indicate a decreased probability of mortality of the subject.

In the determination of the AUROC c statistic, risk genes are assigned a positive value in the test algorithm, so that their expression values lead to an increase in the risk score, which corresponds to higher risk of Death within 5 years. Protective genes are assigned a value

45 negative in the test algorithm, so that its expression values lead to a decrease in the risk score, which corresponds to a lower risk of death within 5 years. Additionally, each gene expression value can be weighted to represent the relative contribution of genes to the risk of patient mortality. It should be understood that large coefficients represent genes that are very important in determining the patient's outcome, while smaller coefficients represent genes that contribute less to the determination of the patient's outcome. The weighted values of the combination of risk and protection genes in the eleven gene panel described herein can be used to calculate the AUROC c statistic.

Reports

In another aspect, the invention presents a report indicating a prognosis of a subject with cancer. The report may, for example, be in an electronic format or on paper. The report may include basic patient information, including a patient identifier (for example, the subject's name, social security number, medical insurance number or a randomly generated number), physical characteristics of the subject (for example , age, weight or sex), the name of the requesting physician, the date on which the prognosis was generated and the date of collection of the sample. The reported prognosis may be related to the probability of survival for a certain period of time, the probability of response to certain treatments within a certain period of time (for example, chemotherapeutic or surgical treatments) and / or the probability of cancer recurrence. . The reported prognosis may be in the form of a percentage probability of

65 survival for a certain period of time, a percentage probability of favorable treatment response (the favorable response can be defined, for example, as tumor contraction or slowing of the

tumor growth) or recurrence over a defined period of time (for example, 20% chance of survival over a period of five years). The informed forecast may, alternatively, be in the form of a calculated score. A higher or lower score, for example, may be indicative of a favorable prognosis. In another embodiment, the reported prognosis may be a general description of the probability of

5 survival, response to treatment or recurrence over a period of time (for example, very likely, likely or unlikely to survive for five years). In another embodiment, the reported forecast may be in the form of a graph. In addition to levels of gene expression, the reported prognosis may also take into account additional characteristics of the subject (for example, age, stage of cancer, gender, previous treatment, physical condition, cardiovascular health and mental health).

In addition to a forecast, the report may optionally include raw data referring to the expression level of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A.

Compositions, kits and integrated systems

Integrated compositions, kits and systems are described herein for the practice of the assays described herein, which use specific antibodies to the specific polypeptides or nucleic acids for the polynucleotides of the invention.

The kits for carrying out the diagnostic assays of the invention usually include a probe comprising an antibody or nucleic acid sequence that specifically binds to the polypeptides or polynucleotides of the invention, and a marker for detecting the presence of the probe. This kit may include several antibodies or polynucleotide sequences encoding polypeptides of the invention, for example, a cocktail of antibodies that recognize proteins encoded by the biomarkers of the invention.

Treatment plans

After a prognosis that provides a low, intermediate or high risk assessment of 5-year mortality, a method for determining a treatment plan can be devised. For example, once the risk assessment class has been determined, a specific treatment plan for the risk group can be developed. For example, for an individual who has an expression profile of the 11 genes described in this document indicative of a high risk assessment of 5-year mortality, a healthcare professional may use a more aggressive treatment. For an individual who has an expression profile of the 11 genes described in this document indicative of a low mortality risk assessment, a healthcare professional may

35 use a less aggressive treatment. For an individual who has an expression profile of the 11 genes described in this document indicative of an intermediate risk assessment of mortality, a healthcare professional may use a treatment that is not as aggressive as the case of a high risk assessment, but more aggressive than in the case of a low risk assessment of mortality at 5 years.

Computer implemented system

Figure 8 is a block diagram illustrating a computer system 100, on which the embodiments of the present teachings can be implemented. In various embodiments, the computer system 100 may include a bus 102 or other communication mechanism for communicating information, and a processor 104

45 coupled with bus 102 to process information. In various embodiments, the computer system 100 may also include a memory 106, which may be a direct access memory (RAM) or other dynamic storage device, coupled to the bus 102 to determine the base calls and instructions to be executed by the processor 104. Memory 106 can also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by processor 104. In various embodiments, computer system 100 may additionally include a read-only memory (ROM). 108 or other static storage device coupled to bus 102, for storing static information and instructions for processor 104. A storage device 110, such as a magnetic disk or an optical disk, can be provided and coupled to bus 102 for storing information and instructions.

In various embodiments, the computer system 100 can be coupled via bus 102 to a screen 112, such as a cathode ray tube (CRT) or liquid crystal display (LCD), to present information to the user of the computer. An input device 114, which includes alphanumeric and other keys, can be coupled to the bus 102 to communicate information and command selections to the processor 104. Another type of input device for the user is a cursor control 116, such as a mouse, a ball mouse or keys for the direction of the cursor, to communicate address information and command selection to the processor 104 and to control the movement of the cursor on the screen 112. This input device normally has two degrees of freedom on two axes, a first axis (i.e., x) and a second axis (i.e., y), which allows the device to specify positions in a plane.

A computer system 100 can perform the present teachings. Concordantly with certain

65 implementations of the present teachings, computer system 100 may provide results in response to processor 104, which executes one or more sequences of one or more instructions contained in the

Memory 106. Such instructions can be read in the memory 106 of another computer readable medium, such as a storage device 110. The execution of the instruction sequences contained in the memory 106 may cause the processor 104 to perform the procedures described in the present document Alternatively, a wired circuitry may be used instead of, or in combination with, the instructions of the

5 software to implement the present teachings. Therefore, the implementations of the present teachings are not limited to any specific combination of hardware and software circuitry.

Examples

The following examples are offered to illustrate, but not to limit, the claimed invention.

Example 1: Prognostic trial to determine the risk score in patients with non-small cell lung cancer (NSCLC)

15 A prognostic trial was developed based on the expression patterns of 426 patients who had undergone the removal of a non-small cell lung cancer (NSCLC) in stage I-IV at the University of California, San Francisco, to generate a score of useful risk from the clinical point of view. A trial was sought that tended to assign a higher average risk score for patients who had succumbed to their cancer than for those who had survived the follow-up period. Patients whose sample received

20 higher risk scores would be considered at increased risk of death within a period of 5 years after the operation, while patients whose samples received a low score would be more likely to have survived during this time interval after their operations. .

RNA was extracted from the FFIP tissue samples and expression levels were evaluated for the target genes

25 related to the patient's prognosis. Then, a prognostic trial was developed by correlating the expression patterns of the 11 target genes related to the patient's prognosis (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR, WNT3A) with the results of 5-year overall survival using Cox's proportional instantaneous risk model. 337 patients had non-squamous cell NSCLC. A 10-fold multiple cross validation combination that maximizes the concordance rate

30 survival, as well as the Cox proportional instantaneous risk model penalized with L2 produced coefficients for each of the 11 target genes. These coefficients penalized with L2 are shown in Table 1.

Table 1: Coefficients for each of the marker survival predictors

Predictor

Coefficient

b_cotXbag

-0.0023688

b_cotXbrc

-0.1460735

b_cotXcdc

-0.0833502

b_cotXcdk

-0.1865318

b_cotXerb

0.0663762

b_cotXfut

-0.0345802

b_cotXil1

-0.0138303

b_cotXlck

0.2098769

b_cotXrnd

-0.0884906

b_cotXsh3

0.1982098

b_cotXwnt

0,1185592

b_cscXcdk

-0,2019083

b_cscXerb

0.2257878

b_cscXfut

0,0264782

b_cscXlck

0.2306624

35 Using this model, a risk score was obtained for each patient by inserting the expression levels of each of the 11 prognostic genes in the following risk scoring algorithm. The risk scores are shown in Table 2. This risk score is a continuous risk score, with an interval between 1 and 100 in the UCSF algorithm development cohort. An increasing risk score is

40 correlates with increasing changes in mortality within 5 years from the date of tumor removal. The ratio of instantaneous risks of the continuous risk score is shown in Table 3. The RRI of the risk score as a continuous variable is 1,184, which corresponds to an 18% increase in the risk of death within 5 years from surgical removal for each point of increase in the risk score (95% CI: 1,123 to 1,248, p <0.0005).

Table 2: Risk score

Risk score = e ^ (

-0.0023688 * cot * ddct BAG1-1 +

-0.1460735 * cot * ddct BRCA1-1 +

-0.0833502 * cot * ddct CDC6-3 +

-0.1865318 * cot * ddct CDK2AP1-2 +

0.0663762 * cot * ddct ERBB3-1 +

-0.0345802 * cot * ddct FUT3-1 +

-0.0138303 * cot * ddct IL11-1 +

0.2098769 * cot * ddct LCK-3 +

-0.0884906 * cot * ddct RND3-3 +

0.1982098 * cot * ddct SH3BGR-6 +

0,1185592 * cot * ddct WNT3A-1 +

-0.2019083 * csc * ddct CDK2AP1-2 +

0.2257878 * csc * ddct ERBB3-1 +

0.0264782 * csc * ddct FUT3-1 +

0.2306624 * csc * ddct LCK3)

Table 3: Reason for instantaneous risks of the continuous risk score

Cox Regression - Breslow method for draws

No. of subjects = 426

Number of obs. = 426

No. of failures = 187

Time at risk = 17366,98426

LR chi2 (1) = 28.10

Log of probability = -1058,448

Prob> chi2 = 0.0000

t

Risk reason inst. Err. Typ. z P> [z] [Conf. 95%]

punctuation

1,183853, 0319197 6.26 0.000 1.122916 1.248097

5 The risk groups were also developed based on these risk scores, placing patients in different risk categories according to their risk score. For example, cut-off points were obtained by tertile by placing patients in the "low risk", "intermediate risk" or "high risk" groups based on their risk scores. For the cohort of the development of the UCSF algorithm of 426 patients, these tertile cut-off points in the 337 patients with non-squamous NSCLC were 2.7440550 for the risk cut-off point

10 low and 3,8221440 for the cut-off point for high risk.

To assess the accuracy of these risk group assignments, Kaplan-Meier survival curves were generated for the risk groups. The results of Kaplan-Meier survival of these risk groups are shown in Figure 3 and Tables 4-9.

Table 4: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the entire development cohort of the UCSF algorithm of 426.

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.]

risk tertile = 0

0 0 0 1,0000. .

12 138 5 0.9648 0.0155 0.9175 0.9852

24 125 13 0.8732 0.0279 0.8064 0.9182

36 111 4 0.8448 0.0304 0.7740 0.8950

48 87 10 0.7629 0.0369 0.6810 0.8264

60 72 3 0.7338 0.0391 0.6480 0.8019

risk tertile = 1

0 0 0 1,0000. .

12 140 5 0.9653 0.0153 0.9186 0.9854

24 116 23 0.8055 0.0330 0.7309 0.8614

36 101 9 0.7417 0.0366 0.6617 0.8056

48 70 13 0.6356 0.0417 0.5478 0.7108

60 50 7 0.5673 0.0445 0.4754 0.6490

risk tertile = 2

0 0 0 1,0000. .

12 104 37 0.7357 0.0373 0.6543 0.8008

24 73 31 0.5143 0.0422 0.4287 0.5933

36 52 16 0.3966 0.0416 0.3150 0.4769

48 44 3 0.3722 0.0414 0.2917 0.4526

60 28 8 0.2956 0.0409 0.2182 0.3769

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

risk tertile

observed expected

0

35 72.61

one

57 67.92

2

95 46.47

Total

187 187.00

chi2 (2) = 72.61

Pr> chi2 = 0.0000

Test for the trend of survival functions

chi2 (1) = 64.88

Pr> chi2 = 0.0000

Table 5: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the 278 patients with adenocarcinoma in the UCSF algorithm development cohort.

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.)

risk tertile = 0

0 0 0 1,0000. . .

12 99 1 0.9899 0.0100 0.9305 0.9986

24 92 7 0.9192 0.0274 0.8449 0.9587

36 81 3 0.8884 0.0317 0.8075 0.9366

48 61 7 0.8042 0.0418 0.7063 0.8724

60 52 1 0.7890 0.0437 0.6877 0.8608

risk tertile = 1

0 0 0 1,0000. . .

12 95 1 0.9895 0.0105 0.9276 0.9985

24 79 15 0.8314 0.0384 0.7397 0.8931

36 70 7 0.7562 0.0442 0.6563 0.8308

48 46 7 0.6649 0.0508 0.5549 0.7537

60 33 5 0.5841 0.0561 0.4662 0.6846

risk tertile = 2

0 0 0 1,0000. .

12 66 19 0.7738 0.0456 0.6686 0.8493

24 45 21 0.5238 0.0545 0.4122 0.6239

36 30 11 0.3867 0.0538 0.2820 0.4900

48 26 1 0.3728 0.0536 0.2691 0.4764

risk tertile = 2

60 15 4 0.3052 0.0537 0.2045 0.4117

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

risk tertile

observed expected

0

19 45.88

one

35 39.78

Logarithmic range test for equal survival functions

2

56 24.34

Total

110 110.00

chi2 (2) = 58.13

Pr> chi2 = 0.0000

Test for the trend of survival functions

chi2 (1) = 52.45

Pr> chi2 = 0.0000

Table 6: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the 89 patients with squamous carcinoma in the algorithm development cohort of the UCSF

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.]

risk tertile = 0

0 0 0 1,0000

12 28 3 0.9000 0.0548 0.7212 0.9666

24 23 5 0.7333 0.0807 0.5369 0.8567

36 22 0 0.7333 0.0807 0.5369 0.8567

48 19 2 0.6600 0.0877 0.4593 0.8010

60 13 2 0.5844 0.0926 0.3838 0.7398

risk tertile = 1

0 0 0 1,0000.

12 28 3 0.9000 0.0548 0.7212 0.9666

24 22 6 0.7000 0.0837 0.5026 0.8312

36 18 2 0.6296 0.0889 0.4311 0.7754

48 14 4 0.4815 0.0939 0.2914 0.6484

60 10 1 0.4444 0.0937 0.2593 0.6144

risk tertile = 2

0 0 0 1,0000. . .

12 20 10 0.6552 0.0883 0.4541 0.7973

24 14 6 0.4483 0.0923 0.2652 0.6157

36 11 2 0,3793 0.0901 0.2087 0.5490

48 10 0 0.3793 0.0901 0.2087 0.5490

60 7 2 0.2950 0.0876 0.1400 0.4687

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

risk tertile

observed expected

0

12 18.64

one

16 17.14

2

20 12.22

Total

48 48.00

chi2 (2) = 7.46

Pr> chi2 = 0.0240

Test for the trend of survival functions

chi2 (1) = 6.99

Pr> chi2 = 0.0082

Table 7: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the 267 patients with stage I NSCLC in the UCSF algorithm development cohort.

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.]

risk tertile = 0

0 0 0 1.0000.

12 98 5 0.9510 0.0214 0.8862 0.9793

24 90 8 0.8725 0.0330 0.7907 0.9239

36 81 1 0.8627 0.0341 0.7793 0.9163

48 66 6 0.7959 0.0409 0.7011 0.8635

60 54 1 0.7821 0.0425 0.6846 0.8527

risk tertile = 1 1

0 0 0 1,0000. . .

12 94 4 0.9588 0.0202 0.8939 0.9843

24 78 15 0.8040 0.0403 0.7101 0.8702

36 70 5 0.7512 0.0441 0.6521 0.8258

48 50 8 0.6565 0.0497 0.5495 0.7440

60 32 7 0.5559 0.0548 0.4421 0.6554

risk tertile-2

0 0 0 1,0000. .

12 55 14 0.7941 0.0490 0.6773 0.8725

24 43 12 0.6176 0.0589 0.4915 0.7212

36 31 8 0.4924 0.0615 0.3676 0.6057

48 28 1 0.4760 0.0616 0.3518 0.5901

60 15 7 0.3367 0.0625 0.2185 0.4588

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

risk tertile

observed expected

0

21 43.59

one

39 37.53

2

42 20.88

Total

102 102.00

chi2 (2) = 33.33

Pr> chi2 = 0.0000

Test for the trend of survival functions

chi2 (1) = 32.36

Pr> chi2 = 0.0000

Table 8: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the 71 patients with stage II NSCLC in the UCSF algorithm development cohort.

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.]

risk tertile = 0

0 0 0 1,0000

12 0 0 1,0000

24 13 2 0.8571 0.0935 0.5394 0.9622

36 13 0 0.8571 0.0935 0.5394 0.9622

48 9 1 0.7619 0.1224 0.4209 0.9181

60 8 0 0.7619 0.1224 0.4209 0.9181

risk tertile = 1

0 0 0 1,0000

12 0 0 1,0000. . .

24 19 4 0.8182 0.0822 0.5853 0.9276

36 17 2 0.7273 0.0950 0.4910 0.8671

48 12 4 0.5455 0.1062 0.3207 0.7239

60 10 0 0.5455 0.1062 0.3207 0.7239

risk tertile = 2

0 0 0 1,0000.

12 28 8 0.7714 0.0710 0.5946 0.8785

24 17 11 0.4571 0.0842 0.2890 0.6105

36 13 4 0.3429 0.0802 0.1934 0.4979

48 11 1 0.3117 0.0788 0.1682 0.4666

60 7 1 0.2771 0.0772 0.1402 0.4324

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

Risk Tertile

observed expected

0

3 9.24

one

10 14.10

2

25 14.66

Total

38 38.00

chi2 (2) = 12.89

Pr> chi2 = 0.0016

Test for the trend of survival functions

chi2 (1) = 12.04

Pr> chi2 = 0.0005

Table 9: Trend of the logarithmic range test for low, intermediate and high risk patients grouped by risk score in the 69 patients with stage III NSCLC in the UCSF algorithm development cohort.

Start Error function

Total Time Survival Failure Typ. [Int. of 95% Conf.]

risk tertile = 0

0 0 0 1,0000

12 0 0 1,0000. .

24 19 2 0.9000 0.0671 0.6560 0.9740

36 15 2 0.8000 0.0894 0.5511 0.9198

48 12 1 0.7333 0.1039 0.4680 0.8810

60 8 2 0.5926 0.1230 0.3204 0.7862

risk tertile = 1

0 0 0 1,0000

12 22 1 0.9545 0.0444 0.7187 0.9935

24 18 4 0.7727 0.0893 0.5374 0.8985

36 13 2 0.6818 0.0993 0.4462 0.8338

48 8 1 0.6198 0.1079 0.3768 0.7911

60 7 0 0.6198 0.1079 0.3768 0.7911

risk tertile = 2

0 0 0 1,0000. . .

12 17 11 0.5926 0.0946 0.3863 0.7499

24 13 4 0.4444 0.0956 0.2556 0.6175

36 8 4 0.2963 0.0879 0.1406 0.4703

48 5 1 0.2469 0.0860 0.1024 0.4238

60 4 0 0.2469 0.0860 0.1024 0.4238

Note: The survival function is calculated on the complete data and is evaluated at the indicated times; It is not calculated from the aggregates shown on the left.

Logarithmic range test for equal survival functions

Events Events

risk tertile

observed expected

0

7 12.78

one

8 12.66

2

20 9.56

Total

35 35.00

chi2 (2) = 16.03

Pr> chi2 = 0.0003

Test for the trend of survival functions

chi2 (1) = 12.16

Pr> chi2 = 0.0005

Example 2: Test of 11 genes to predict survival in non-squamous non-small cell lung cancer removed

5 A 14-gene trial was developed using a quantitative PCR analysis of paraffin-fixed formalin-fixed tissues (FFIP) with a cohort of 361 patients with non-squamous NSCLC extracted at the University of California, San Francisco (UCSF). This trial included eleven biomarkers (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A) and three constitutive genes (ESD, TBP, YAP1).

10 This trial, developed and carried out in an independent laboratory certified by Clinical Laboratory Improvement Amendments (CLIA) was later validated by the Kaiser Permanente Division of Research (KPDOR ) with a study with concealment designed in a cohort of 433 patients with stage I non-squamous NSCLC removed in the hospitals of the Kaiser Permanente Northern California system (CA, USA). The KPDOR compared in a way

15 independent trial results with actual patient results. An independent and international large-scale validation of this molecular prognostic trial was also performed in a cohort of 1006 Chinese patients who had undergone the removal of an early stage NSCLC in one of several institutions participating in the China Clinical Trials Consortium (CCTC) (China Clinical Trials Consortium).

20 Patients were eligible to enter the study as part of the training cohort if they had undergone a surgical removal of a non-squamous NSCLC in the UCSF with curative intent, between January 1, 1997 and December 31, 2007

25 Patients were eligible to be included in the Kaiser Permanente validation cohort if they had undergone the complete removal of a stage I non-squamous NSCLC from the American Joint Commission on Cancer for a clinical staging and pathological in a Kaiser Permanente facility in Northern California between January 1, 1998 and December 31, 2005.

30 Patients were eligible to be included in the CCTC validation cohort if they had undergone an attempt to curatively remove a non-squamous NSCLC in stage I-III of the American Joint Commission on Cancer, at the First Affiliated Hospital of Guangzhou Medical College (Guangdong, China), Sun Yat-sen University Cancer Center in Guangzhou (Guangdong, China) or Shanghai Pulmonary Hospital (Shanghai, China) between January 1, 2000 and December 31, 2008.

Exclusion criteria for patients in either the training cohort or the validation cohort were the following: absent or inadequate tissue blocks (i.e., a tumor that occupies <25% of the tissue surface area), death within 30 days after removal, preoperative treatment with chemotherapy (only validation cohorts), positive margins in the pathology analysis (only validation cohorts) and a 5 second cancer (excluding basal and squamous cell carcinomas) diagnosed within 3 years since the diagnosis of lung cancer (only CCTC validation cohort). Information on clinical variables, follow-up and cause of death were obtained from a review of medical records. Vital status and date of death were established by reviewing medical records and verified by sources that included the Kaiser Permanente Northern California Cancer Registry, California Death

10 Records, Social Security Death Master File and direct contact with the patient or his family.

Sample preparation and analysis

Six 10-micrometer FFIP cuts per sample were used. The samples were cleaned of paraffin by

Use xylene and then incubate with proteinase K (MasterPure RNA Purification Kit, Epicenter, Madison, WI) at 65 ° C for 2 hours. Protein precipitation and alcohol-based nucleic acid precipitation were performed using the MasterPure RNA purification kit (Epicenter, Madison, WI). RNA extracts were treated with DNase and purified using silica gel membrane centrifugal columns (Kit RNEeasy Micro, Qiagen, Valencia, CA).

20 To control the degradation of the RNA that could occur in the FFIP samples, the quantity and quality of the RNA was measured using a Nanodrop spectrophotometer (Thermo Scientific, Wilmington, DE). The extracted RNA was subjected to reverse transcription (Kit iScript Select cDNA Synthesis, BioRad Laboratories, Hercules, CA) using gene specific primers. The gene-specific primers were truncated versions in 9-13 numbers of the

25 reverse PCRq primers optimized for a pairing temperature of 42 ° C. The cDNA was subjected to 10 preamp cycles (TaqMan PreAmp Master Mix, Applied Biosystems, Carlsbad, CA) before the PCRq.

TaqMan quantitative PCR assays (BioSearch Technologies, Novato, CA) designed at the request of the

30 client for use with RNA extracted from FFIP tissues to quantify RNA expression using FAST chemistry in a 7900HT Fast real-time PCR System (Applied Biosystems, Carlsbad, CA). Specific TaqMan PCR assays designed for FFIP were designed to target amplicons of 65-85 base pairs that crossed the exon-exon boundaries, which avoided mold structures and cross homologies (Beacon Designer 5.0, Premier Biosoft, Palo Alto , CA). All primer sequences were subjected to

35 BLAST search against the human genome (NCBI ref_assembly 37.1) to ensure target specificity. The synthesized primers were analyzed for the optimal concentration of the primers and the dissociation of individual product using SYBR green melting curves.

All measurements of RNA expression were normalized with respect to commercially available RNA

40 extracted from clustered frozen normal lung samples (Clontech Laboratories, Mountain View, CA) and the relative expression for each target gene was calculated using the comparative CT method. The average CT value of the three constitutive genes ESD, TBP and YAP1 was used to normalize gene expression and calculate CT delta values.

45 Development of a forecasting algorithm

Eleven cancer-related target genes (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A) and three reference genes (ESD, TBP and YAP1) were evaluated in the UCSF training cohort. ). See Figure 4.

50 The Cox proportional instantaneous risk model penalized with L2 (package R glmnet vl.5.3) was the main analytical tool used to develop the coefficients in the prognostic algorithm, using the relative expression values of the eleven target genes in the cohort of FFIP of the UCSF. The amount of L2 penalty applied was determined using a 10-fold cross validation. A score of

55 continuous risk for each subject, based on the model coefficients. A conventional linear risk model was used in which each level of relative gene expression (delta-delta CT) was multiplied by its coefficient; These products were added later, resulting in a single unprocessed risk score.

Unprocessed Risk Score = delta-delta CT BAG1 * model coefficient BAG1 + delta-delta CT BRCA1 *

60 model coefficient BRCA1 + delta-delta CT CDC6 * model coefficient CDC6 + delta-delta CT CDK2AP1 * model coefficient CDK2AP1 + delta-delta CT ERBB3 * model coefficient ERBB3 + delta-delta CT FUT3 * model coefficient FUT3 + delta -delta CT IL11 * model coefficient IL11 + delta-delta CTLCK * model coefficient LCK + deltadelta CT RND3 * model coefficient RND3 + delta-delta CT SH3BGR * model coefficient SH3BGR + delta-delta CT WNT3A * model coefficient WNT3A.

Then, the unprocessed risk score was adjusted to scale using a linear function based on the minimum and maximum unprocessed risk scores, producing a single integrated risk score that was included in a 1-100 scale.

5 Risk Score = 39,39747 * Unprocessed Risk Score - 16,94965 + 1

The resulting predicted risk scores were divided into the 33rd and 67th percentiles to generate low, intermediate and high risk groups. Table 10 below provides a complete table of the algorithm coefficients, the scale adjustment coefficients and the values of the cut-off points of the categories

10 risk.

Table 10: Summary of the algorithm coefficients, scale adjustment coefficients and cut-off point values of the risk categories obtained from the UCSF training cohort

Value

Gene coefficients

BAG1 -0.0023688

BRCA1 -0.1460735

CDC6 -0.0833502

CDK2AP1 -0.1865318

ERBB3 0.0663762

FUT3 -0.0345802

IL11 -0.0138303

LCK 0.2098769

RND3 -0.0884906

SH3RGR 0.1982098

WTN3A 0,1185592

Coefficients of scale adjustment

Pending 39,39747

Intersection -16,94965

Cut-off values of risk categories

Low-intermediate cut-off point 23,81960

Intermediate-high cut point 36.87494

15 Risk scores were generated and risk categories were assigned using the same gene coefficients, scale adjustment coefficients and cut-off point values in the validation cohorts. Since the limits of the risk score scale were 1 and 100, based on the UCSF training cohort interval, any risk score of less than 1 was assigned to a risk score of

20 1 while any risk score greater than 100 was assigned to the risk score of 100.

Robustness and validation of the test

The molecular assay was developed, fully specified and validated analytically in a laboratory

25 certified by the CLIA before the start of the clinical validation study with concealment. The concentration per nanodrop and the values of the purity cut-off points for the RNA were determined empirically by evaluating more than 400 samples whose average unprocessed constitutive gene expression values were included within a range of pre-specified unprocessed CT. The concentration, the 260/280 ratio and the cutoff points of the 260/230 ratio were determined by eliminating the lowest 2.5% for each individual parameter

30 ranked from lowest to highest and then taking the next highest measure as the acceptable cut-off number. In addition, the CT of the average unprocessed constitutive gene was calculated for each sample. Only samples whose average unprocessed expression values of the constitutive genes were included within a prespecified range were included in the study. To obtain this interval, the expression of 442 FFIP lung cancer samples obtained from the UCSF was studied. The CT value of the average constitutive gene for these

35 samples varied between 17.79-37.76, with an average value of 23.94 and a standard deviation of 2.66. The prespecified range was determined taking into account the mean CT of the constitutive gene plus or minus three typical deviations.

Each PCR plate was processed with a positive control (RNA extracted from frozen and pooled normal lung samples, commercially available (Clontech Laboratories, Mountain View, CA) as well as with a negative control (without mold). Each sample was processed with a TaqMan assay designed to detect genomic contamination. Repeated analysis on different days and in different cuts of the molecular prognostic test of FFIP samples of the

The same tumor showed a high reproducibility of the risk score, with an average standard deviation of 2.18 units (range between 0.83-4.62) on a scale of 100 units.

The inclusion criteria of more than 25% of tumor were obtained empirically by categorizing samples in 25-50% of tumor, 50-75% of tumor and> 75% of tumor. The ratio of instantaneous risks of the high risk group 10 compared to the low risk group for each of these percentage categories is shown in Table 1 below.

Table 11: Reason for instantaneous risks of the high risk category per percentage tumor

RRI percentage tumor of risk category § 95% CI p-value

25-50% 2.39 1.65-3.47 <0.0001

50-75% 2.31 1.57-3.28 <0.0001

> 75% 2.40 1.52-3.79 <0.0001 §Model as a continuous variable

Statistic analysis

The overall survival from the moment of excision was chosen as the main assessment criterion. A secondary assessment criterion in the Kaiser validation cohort was cancer-specific mortality of

20 lung The main predictor evaluated was the risk category assigned by the molecular test. Other important covariates, including age, sex, history as a smoker, histopathology, tumor size and stage of the disease, were compared with the result using a model of instantaneous Cox proportional hazards of one variable or multiple variables. Wald and nested likelihood ratio tests were performed for modeling of one variable and multiple variables, respectively, to assess the

25 statistical significance. Nested probability ratio tests are more appropriate for multi-variable models because they examine whether the addition of a new variable, such as molecular testing, offers an improvement in fit over conventional clinical variables such as Age, sex and tumor size. A stratified Kaplan-Meier analysis with a data set with right-hand censorship and the logarithmic range test were used for the trend to assess the association between the risk category and

30 the main and secondary assessment criteria. For all statistical analyzes, a prespecified two-tailed α of 0.05 was considered statistically significant. The area under the curve of the analysis of the operational characteristic of the time-dependent receiver (AUROC) was calculated with the survcomp package (version 1.1.6) in R; Differences in AUROCs were analyzed using the Cox proportional instantaneous risk model of multiple variables and compared by using the integrated AUROCs with the sum test.

35 of Wilcoxon ranges.

A calculation of the power of the UCSF training cohorts and of Kaiser Permanente validation was performed using the following assumptions: β of 0.9, standard deviation of the risk category population of 0.8, a risk ratio 1.5 snapshots for the high risk group, 0.2 withdrawal probability and

40 event probabilities of 0.4 (UCSF training cohort in stage I-III) and 0.3 (Kaiser validation cohort in stage I). The power calculation resulted in an estimated sample size of 313 and 417 patients for the UCSF and Kaiser Permanente cohorts respectively. As the proportion of satisfactory RNA extraction was not known before the start of the study, a failure rate of 10% was assumed, resulting in a final sample size of 344 patients for the UCSF training cohort.

45 The analyzes were performed with the programming languages R29 (version 2.12.2 for Macintosh) and Stata / MP (version 11).

Results

50 A total of 399 patients who had undergone excision of a non-squamous NSCLC in the UCSF during the study period were identified; of these, 361 met the inclusion criteria in the training cohort. 460 Kaiser patients from Northern California had undergone the removal of a non-squamous NSCLC in phase, of whom 433 met the criteria for inclusion in the independent validation cohort. 1006 were identified

55 patients in the CCTC institutions that met the inclusion criteria in that validation study. The relevant clinical and pathological characteristics of these patients are shown below in Table 12. The satisfactory RNA extraction rate was high in the three cohorts.

Table 12: Clinical and pathological characteristics of the patients

Kaiser CCTC UCSF Validation Training Validation Cohort Validation Cohort Cohort

Available FFIP blocks 361 433 1006 Successful RNA recovery 337 (93%) 420 (97%) 967 (96%) Age at the time of removal 67.4 (11) 66.6 (9) 58.3 (11)

(years, average [DT]) Sex (female) 200 (59%) 229 (55%) 366 (38%)

Background as a smoker

Yes 224 (66%) 355 (85%) 492 (49%)

No 57 (17%) 36 (9%) 403 (40%)

Unknown 56 (17%) 29: (7%) 72 (7%) Survival follow-up 64.0 (45.6-88.9) 106.0 (88.0-125.0) 53-4 (37.4-68.0) (months; medium [amplitude interquartile]) Deaths at 5 years from 139 (41%) 179 (43%) 406 (42%) removal Histopathology

Adenocarcinoma 278 (83%) 325 (77%) 881 (88%)

Large cells 17 (5%) 15 (4%) 17 (2%)

Mixed 10 (3%) 15 (4%) 46 (5%)

NSCLC (if not otherwise specified) 32 (10%) 65 (16%) 23 (2%)

Stadium

I 223 (66%) 420 (100%) 471 (47%) Ia 152 (45%) 285 (68%) 239 (24%) Ib 71 (21%) 135 (32%) 232 (23%)

II 41 (12%) 0 222 (22%) IIa 14 (4%) 0 69 (7%) IIb 27 (8%) 0 153 (15%)

III 58 (17%) 0 266 (26%) IIIa * 32 (10%) 0 247 (25%) IIIb 26 (8%) 0 19 (2%) IV 9 (3%) 0 0 Indeterminate 6 (2% ) 0 8 (1%) The data is n (%), unless otherwise stated. CCTC = China Clinical Trails Consortium. FFIP = fixed in formalin, included in paraffin. NSCLC = small cell lung cancer. UCSF = University of California, San Francisco.

During the rigorous technical validation and trial establishment, the candidate gene expression analysis and the comparison with the results of the patients showed to be similar in the groups of tissue blocks in which the tumor occupied either 25-50%, 50-75% or more than 75% of the tissue surface area.

Individual risk scores were calculated for each patient in the UCSF training cohort. Higher risk scores were positively associated with an increased probability.

10 mortality at 5 years. With reference to Figures 5A and 5B, it should be understood that an instantaneous risk ratio greater than 1 implies that more patients in the high-risk group are dying at any time compared to the low-risk group, while a proportion of risks Snapshots less than 1 means that fewer patients in the high-risk group are dying at any time compared to the low-risk group.

15 To better identify patients at the highest or lowest risk, cut-off values were obtained that define the low-risk, intermediate-risk and high-risk groups by dividing the risk scores of the training cohort into tertiles. With reference again to Figures 5A and 5B, a higher mortality risk was observed with each increase in the risk category in almost every subgroup of the training cohort of the

UCSF and the correlation of the risk score with the clinical outcome were very similar in the samples with 2550%, 50-75% and> 75% of tumor surface area.

After the test was fully specified, the CLIA certified laboratory performed the

5 technical validation. The KPDOR and the CCTC then carried out independent concealment validations of the validated test in a technical way. The KPDOR sent the samples to the CLIA-certified laboratory for analysis with concealment and assignment to the risk category, and the performance of the test. On the contrary, with the empirical reason

(1: 1: 1) of patients with low risk, intermediate risk and high risk in the training cohort, a higher proportion of high risk patients were identified in the validation cohort of Kaiser Permanente.

10 This finding could be attributed to the overall 5-year overall survival of the Kaiser stage I validation cohort (56.4%) compared to the UCSF stage I training cohort (61.9%). In the Kaiser Permanente validation cohort, the Kaplan-Meier survival analysis showed a 5-year survival of 71.4% (95% CI 60.5-80.0) in the low-risk group of 58 , 3% (48.9-66.6) in the risk group

15 intermediate and 49.2% (42.2-55.8) in the high risk group. A sensitivity analysis that excluded the 18 patients from the Kaiser Permanente validation cohort who received adjuvant chemotherapy provided 5-year survival results that were very similar: 70.0% (58.7-78.8) in the low risk group, 58.2% (48.5-66.7) in the intermediate risk group and 48.9% (41.7-55.6) in the high risk group (ptend = 0 , 0006), Specific survival for 5-year lung cancer was 84.6% (74.4-91.0) in the group of

20 low risk, 70.3% (60.6-78.0) in the intermediate risk group and 63.3% (55.8-69.8) in the high risk group. A Kaplan-Meier survival analysis was also performed for patients with stage I disease in the Kaiser validation cohort who did not have a high risk National Comprehensive Cancer Network (NCCN) criteria. This group included all patients with stage IA disease and a subgroup of patients with stage IB disease. 5-year overall survival for patients in this subgroup (with

25 the staging of the risk according to the results of the molecular test) was 72.7% (61.3-81.3) in the low risk group, 59.0% (48.9-67.8) in the intermediate risk group and 50.4% (42.0-58.3) in the high risk group.

With reference to Figure 6A, median survival was observed as 113 months in the low risk group,

30 of 91 months in the intermediate risk group and 59 months in the high risk group. With reference to Figure 6B, it was not observed that in any risk group the median survival specific to lung cancer was achieved. The mortality incidence rate was 2.7 per 100 person-years in the low risk group, 5.0 per 100 person-years in the intermediate risk group and 6.6 per 100 person-years in the group of high risk. With reference to Figure 6C, the median survival was 113 months in the low risk group, 88 months in the

35 intermediate risk group and 70 months in the high risk group.

In the Kaiser Permanente validation cohort, the risk category (high), age and sex were statistically significant predictors of mortality in an analysis of a variable, as shown in Table 13 below.

40 Table 13: Cox proportional instantaneous risk model for 5-year overall mortality in the Kaiser Permanente validation cohort

Analysis of a variable Analysis of multiple variables Risk ratio P-value of Risk ratio P-value of the instantaneous test (instantaneous CI test (95% CI) Wald 95%) likelihoods Risk category *

High risk 2.16 (1.39-3.36) 0.0007 2.04 (1.28-3.26) 0.0016

Intermediate risk 1.60 (0.98-2.60) 0.0610 1.66 (1.00-2.74) 0.00436

Age> 65 years 1.55 (1.14-2.10) 0.0054 1.66 (1.21-2.29) 0.0016

Sex (female) 0.55 (0.41-0.74) 0.0001 0.67 (0.49-0.92) 0.0123

Never smoker 0-, 59 (0.32-1.09) 0.0917 0.83 (0.44-1.55) 0.5438

Large cell carcinoma

0-96 (0.42-2.17) 0.9139 0.64 (0.26-1.59) 0.3038

Mixed

0.98 {0.43-2.22) 0.9615 0.85 (0.37-1.95) 0.6932

CPNM (if not specified otherwise)

1.13 (0.89-1.93) 0.1659 1.18 (0.79-1.75) 0.4320

Tumor size> 4 cm

1.42 (0.97-2.07) 0.0697 1.10 (0.73-1.66) 0.6435

NSCLC = non-small cell lung cancer. * Compared to the low risk group. † Compared with adenocarcinoma.

The analysis of multiple variables (adjusting for age, sex, history as a smoker, histopathology and tumor size> 4 cm) showed that both groups of high risk and intermediate risk as well as age and sex were statistically significant predictors of mortality, as see below in Table 14.

Table 14: Cox proportional instantaneous risk model of multiple variables for 5-year overall mortality in the Kaiser Permanente validation cohort excluding molecular test

RRI

95% CI P value of the RV test

Age> 65

1.59 1.26-2.20 0.0036

Female sex

0.65 0.47-0.89 0.0064

Never smoker

0.74 0.40-1.38 0,3294

Histopathology§

Large cell carcinoma

0.76 0.31-1.87 0.5346

Mixed

1.00 0.44-2.27 0.9933

CPNM if nothing else is specified

1.26 0.85-1.88 0.2555

Tumor size> 4 cm

1.21 0.81-1.80 0.3608

§ Compared with adenocarcinoma

In the CCTC cohort, 5-year mortality after complete removal of a non-squamous NSCLC due to

10 risk group (defined according to the results of the molecular test) was as follows: 74.1% (66.0-80.6) in the low risk group, 57.4% (48.3- 65.5) in the intermediate risk group and 44.6% (40.2-48.9) in the high risk group, as shown in Figure 7.

Survival was 101.1 months in the low risk group, 77.2 months in the risk groups.

15 intermediate and 43.1 months in the high risk group. An improvement is suggested with the use of the 14-gene prognostic trial compared to the use of traditional staging by the statistically significant separation of Kaplan-Meier survival curves for 5-year overall survival among low-risk patients, intermediate risk and high risk in the subgroup analysis of patients with different stage disease (see Figure 7): stage I disease (low risk = 83.0% [73.8-89.1], intermediate risk = 67 , 7% [54.8

20 77.7]; high risk = 64.6% [57.9-70.5]), stage II disease (low risk = 54.2% [30.1-73.2]; intermediate risk = 45.8% [26, 2-63.4]; high risk = 38.1% [29.4-46.8]) and stage III disease (low risk = 53.3% [32.6-70.3]; intermediate risk = 43.3% [27.2-58.5]; high risk = 24.0% [17.5-30.9]). Cox proportional instantaneous risk modeling of one variable indicated that sex (male), history as a smoker, histopathology of large and mixed cells and the stage of the disease all had a negative effect on

25 survival in the CCTC cohort, as seen in Table 15 below.

Table 15: Cox proportional instantaneous risk model for 5-year overall mortality in the validation cohort of the China Clinical Trials Consortium

Analysis of a variable Analysis of multiple variables Risk ratio P-value of Risk ratio Instant p-value of the test (instantaneous test IC (Wald's IC

95%) of Wald 95%) Risk Category * High risk 3.07 (2.21-4.25) 0.0001 2.37 (1.63-3.43) <0.0001

Intermediate risk 1.87 (1.26-2.77) 0.0019 1.60 (1.03-2.49) 0.0354

Age> 65 years 1.11 (0.90-1.37) 0.3337 1.19 (0.94-1.49) 0.1493

Sex (female) 0.78 (0.63-0.95) 0.0150 0.93 (0.70-1.23) 0.6057

Never smoker 0.70 (0.56-0.86) 0.0009 0.84 (0.64-1.10) 0.1986

Histopathology † Cell carcinoma

2.12 (1.09-4.11) 0.0259 1.68 (0.83-3.41) 0.1831

Large Mixed 1.67 (1.12-2.48) 0.0118 1.07 (0.69-1.64) 0.7714 NSCLC (if not specified

0.89 (0.44-1.80) 0.7528 0.77 (0.36-1.63) 0.4759

otherwise) Stadium ‡ 1.44 (1.35-1.53) <0.0001 1.43 (1.33-1.53) <0.0001

However, it was not observed that any of these factors had such a large effect on survival as the designation in the high risk category according to the molecular assay. The analysis of multiple variables showed that the designation of high risk and intermediate risk remained a statistically significant predictor of survival even after adjustment for age, sex, history as a smoker, histopathology and disease stage. See Tables 16-18 below.

Table 16: Tabulation of risk factors by risk category in the Kaiser Permanente validation cohort

Low Risk Intermediate Risk High Risk P Value

Number of patients 85 119 216 Age at the time of

66.6 (9.8) § 67.1 (8.9) § 66.2 (9.3) § 0.7114 *

Excision Sex Male 28 (32.9) 51 (42.9) 112 (51.9) 0.0098 Female 57 (67.1) 68 (57.1) 104 (48.1) Background as a smoker Yes 68 (180 , 0) 100 (84.0) 187 (86.6) 0.1096 No 12 (14.1) 10 (8.4) 14 (6.5) Histopathology Adenocarcinoma 77 (90.6) 95 (79.8 ) 153 (70.8) 0.0070

Large cells 0 2 (1.7) 13 (6.0) Mixed 2 (2.4) 3 (2.5) 10 (4.6) NSCLC if not specified

6 (7.1) 19 (16.0) 40 (18.5)

otherwise Tumor Size (cm) 2.3 (1.4) § 2.6 (1.6) § 3.4 (1.8) § <0.0001 * Stage 1a 70 (82.4) 95 (79 , 8) 120 (55.6) <0.0001 1b 15 (17.6) 24 (20.2) 96 (44.4)

The numbers in brackets represent the percentage of patients in each risk category except where be indicated. § Cohort average (standard deviation) * ANOVA test

Table 17: Cox proportional instantaneous risk model of multiple variables for 5-year overall mortality in the validation cohort of the China Clinical Trials Consortium excluding molecular test

P value of the

95% CI RRI

RV test

Age> 65 1.18 0.94-1.49 0.1532 Female sex 0.93 0.70-1.22 0.5917 Never smoker 0.74 0.56-0.97 0.0260 Histopathology§

Large cell carcinoma 1.79 0.89-3.63 0.1374 Mixed 1.15 0.75-1.77 0.5221 NSCLC if not otherwise specified 0.77 0.36-1.63 0, 4707

Phase §§ 1.48 1.38-1.58 <0.0001 § Compared with Adenocarcinoma § § Modeled as a continuous variable

Table 18: Tabulation of risk factors by risk category in the validation cohort of the China Clinical Trials Consortium

Low risk Intermediate risk High risk P Value

Number of Patients 193 172 602 Age at the time of

57.6 (11.4) § 58.5 (10.7) § 58.5 (10.6) § 0.5529 *

Excision Male Sex 98 (50.8) 86 (50.0) 417 (69.3) <0.0001 Female 95 (49.2) 86 (50.0) 185 (30.7) Smoking History Yes 70 ( 38.3) 68 (43.9) 354 (63.6) <0.0001 No 113 (61.7) 87 (56.1) 203 (36.4) Histopathology Adenocarcinoma 186 (96.4) 159 (92 , 4) 536 (89.0) 0.0032

Large Cells 0 4 (2,3) 13 (2,2) Mixed 4 (2.1) 3 (1.7) 43 (7.1) NSCLC if I don't know

3 (1.6) 6 (3.5) 10 (1.7)

otherwise specify Tumor size (cm) 2.8 (1.4) § 3.4 (1.7) § 4.3 (4.1) § <0.0001 * Stage I 130 (68.4) 95 ( 55.6) 246 (41.1) II 34 (17.9) 32 (18.7) 156 (26.1) <0.0001 III 26 (13.7) 44 (25.7) 196 (32, 8)

The numbers in brackets represent the percentage of patients in each risk category except in Where indicated. § Cohort average (standard deviation) * ANOVA test

In addition to the analysis of multiple variables, a time-dependent AUROC analysis was performed to test whether the

5 molecular assay provides more useful prognostic information than just conventional staging. The AUROC is a measure of the discrimination of a prognostic test and coincides with the statistic c. The NCCN criteria have been used, which identify such patients as stage IB plus at least one of the following risk factors: poorly differentiated tumors, vascular invasion, wedge removal, minimum margins, tumors larger than 4 cm in diameter, involvement of the visceral pleura and unknown state of lymph nodes. In the cohort of

10 validation of Kaiser Permanente the sum of the molecular test provided better risk discrimination than only the NCCN risk criteria, demonstrated by a larger AUROC (statistical c of 0.60 versus 0.54; p <0.0001) . Complete data for all criteria for NCCN high-risk stage I were not available in patients in the CCTC cohort. Therefore, the AUROC analysis in this cohort focused on 471 patients with stage I disease; the addition of the molecular assay to just conventional staging

15 similarly increased the AUROC for this group, in a manner consistent with better discrimination in the prediction of risk by the sum of the molecular test (statistic c of 0.61 versus 0.56; p <0.0001) .

Discussion

20 It was observed that the quantitative PCR-based trial used in the present Example reliably identifies patients with early-stage non-squamous NSCLC at high risk of mortality after surgical excision, discriminating such patients more accurately than using the criteria of NCCN alone. This example demonstrates the implementation of a platform with interpretable RNA extraction from tissue included in formalin-fixed paraffin, performing the test in one of the studies in a laboratory that was independent of

25 laboratory in which the trial was conducted, the very large sizes of the independent validation cohorts and the potentially large disparity between the genetic collections of one of the validation cohorts and those of the original training cohort used for the development of the trial .

The molecular assay used in the present Example provides a more accurate test for the definition of

30 subsets of patients with non-squamous NSCLC and statistically heterogeneous results. This trial was independently validated in a large extrahospital American cohort to improve risk stratification in patients with stage I disease. In view of the great magnitude of the public health crisis due to lung cancer in China, The additional validation of this molecular test in a large population of Chinese further increases its possible effect. This trial provides a prognostic differentiation of

Patients with early stage disease and could be useful in identifying the most appropriate application of treatment guidelines to improve clinical outcomes.

Example 3: Comparison of the AUROC for the 11 gene assay against other assays with different 5 gene combinations

The present example predicts the success of the 11 gene assay compared to other combinations of genes in the assignment of patients to high or low risk categories using the area under the curve of the analysis of the recipient's operational characteristic (AUROC).

10 AUROC analysis was performed on RNA samples taken from 337 patients with stage I-IV lung cancer. As noted in Table 19 below, it was observed that the 11 gene assay is superior to the other combinations of genes tested in the present example, as reflected by a larger AUROC c statistic.

15 Table 19: AUROC values

Gene set

AUROC

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 LCK RND3 SH3BGR WNT3A

0.7215

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 IL11 LCK

0.6321

BAG1 BRCA1 CDC6 RND3

0.5889

BAG1 BRCA1 CDC6 FUT3 IL11 RND3 SH3BGR

0.6438

BAG1 BRCA1 CDC6 CDK2AP1 FUT3 IL11 RND3 SH3BGR

0.6466

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 FUT3 IL11 LCK RND3 SH3BGR SIX3

0.6758

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 IL11 RND3 SH3BGR

0.6489

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 FUT3 IL11 RND3 SH3BGR SIX3 WNT3A

0.6829

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 ERBB3 FUT3 IL11 LCKRND3 SIX3

0.6658

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 RND3

0.6382

BAG1 BRCA1 CDC6 CDK2AP1 FUT3 IL11 LCKRND3 SH3BGR

0.6767

BAG1 BRCA1 CDC6

0.6280

BAG1 BRCA1 CDC6 CDK2AP1 FUT3 IL11 RND3

0.6321

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 RND3 SH3BGR

0.6531

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 LCK RND3 SH3BGR

0.6815

BAG1 BRCA1 CDC6 CDK2AP1 FUT3 IL11 WNT3A

0.6735

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 ERBB3 FUT3 IL11 LCKRND3 SH3BGR SIX3 WNT3A

0.6413

CDK2AP1 FUT3 IL11 RND3

0.6276

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 ERBB3 FUT3 IL11 LCKRND3 SH3BGR SIX3

0.6823

FUT3 IL11 RND3

0.5883

BAG1 CDC6 FUT3 IL11 WNT3A

0.6770

BAG1 BRCA1 CDC6 FUT3 RND3

0.6298

BAG1 BRCA1 CDC6 FUT3 IL11 RND3 SH3BGR

0.6438

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 IL11 RND3

0.6321

BAG1 BRCA1 CDC6 CDK2AP1 EMX2 ERBB3 FUT3 IL11 RND3 SH3BGR SIX3 WNT3A

0.6841

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 RND3 WNT3A

0.6704

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 RND3 SH3BGR WNT3A

0.6842

ERBB3 LCK RND3 WNT3A

0.6861

ERBB3 LCK RND3

0.5821

BAG1 CDC6 FUT3 IL11 RND3

0.6379

Selection of the gene risk property

The 11 gene assay was analyzed to determine which genes conferred risk and which genes conferred protection using Cox's proportional instantaneous risk model. These determinations are listed for each gene in Table 20.

Table 20: Risk property of the 11 gene test genes

Gen

Reason for instant risks Property

BAG1

1,002 Risk

BRCA1

1,157 Risk

CDC6

1,087 Risk

CDK2AP1

1,205 Risk

ERBB3

0.936 Protection

FUT3

1,035 Risk

IL11

1,014 Risk

LCK

0.811 Protection

RND3

1,093 Risk

SH3BGR

0.820 Protection

WNT3A

0.888 Protection

10 Selection of the gene coefficient

The expression values of the 11 Genes (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A) can be combined in an infinite number of ways to provide a unique number that is representative of the risk of mortality of the patient. Each of the expression values of the genes is

15 weighted (as represented by a coefficient) to represent the relative contribution of the gene to a patient mortality risk. The coefficients for each of the 11 gene test genes are listed below in Table 21.

Table 21: Coefficients for genes in the 11 gene assay 20

Gen

Coefficient Property AUROC of the risk score

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 LCK RND3 SH3BGR WNT3A

-0.0024 -0.1461 -0.0834 -0.1865 0.0664 -0.0346 -0.0138 0.2099 -0.0885 0.1982 0.1186 Risk Risk Risk Risk Protection Risk Risk Protection Risk Protection Protection 0.7215

AUROC analysis

The AUROC of the risk score for the test of 11 genes in a cohort of test samples was measured

25 from 337 patients who had undergone lung cancer excision, measuring their risk scores and comparing the risk allocation with the actual 5-year survival results of the patients. The AUROC c statistic, which used the coefficients in Table 21 above, was 0.7215. Based on the AUROC analysis, it was observed that the 11 gene assay outperforms other models (listed with their AUROC c statistics below in Tables 22-31), in which different coefficients were used for

30 weigh the contributions of the 11 genes.

Table 22: Alternative Model No. 1

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.4806 Risk 0.6755

BRCA1

-0.2392 Risk

CDC6

-0.4906 Risk

CDK2AP1

-0.0386 Risk

ERBB3

0.4989 Protection

FUT3

-0.3274 Risk

IL11

-0.2656 Risk

LCK

0.4071 Protection

RND3

-0.2710 Risk

SH3BGR

0.1232 Protection

WNT3A

0.3385 Protection

Table 23: Model No. 2

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.0718 Risk 0.6715

BRCA1

-0.1588 Risk

CDC6

-0.4781 Risk

CDK2AP1

-0,1202 Risk

ERBB3

0.2770 Protection

FUT3

-0,2002 Risk

IL11

-0.2237 Risk

LCK

0.2451 Protection

RND3

-0.4197 Risk

SH3BGR

0,1902 Protection

WNT3A

0.2869 Protection

Table 24: Model # 3

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.1630 Risk 0.6751

BRCA1

-0.0783 Risk

CDC6

-0.4656 Risk

CDK2AP1

-0,2018 Risk

ERBB3

0.0551 Protection

FUT3

-0.0729 Risk

IL11

-0.1818 Risk

LCK

0.0832 Protection

RND3

-0.0685 Risk

SH3BGR

0.2572 Protection

WNT3A

0.2354 Protection

Table 25: Model No. 4

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.4308 Risk 0.6385

BRCA1

-0.1066 Risk

CDC6

-0,2033 Risk

CDK2AP1

-0.1515 Risk

ERBB3

0.2706 Protection

FUT3

-0.4905 Risk

IL11

-0.4776 Risk

LCK

0,1602 Protection

RND3

-0.2419 Risk

SH3BGR

0.4835 Protection

WNT3A

0.2145 Protection

Table 26: Model No. 5

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.0220 Risk 0.6419

BRCA1

-0.0262 Risk

CDC6

-0.1908 Risk

CDK2AP1

-0.2331 Risk

ERBB3

0.0487 Protection

FUT3

-0.3632 Risk

IL11

-0.4358 Risk

LCK

0.4983 Protection

RND3

-0.3906 Risk

SH3BGR

0.0506 Protection

WNT3A

0.1630 Protection

Table 27: Model No. 6

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.0690 Risk 0.6652

BRCA1

-0.0436 Risk

CDC6

-0,1157 Risk

CDK2AP1

-0.2227 Risk

ERBB3

0.2175 Protection

FUT3

-0.0998 Risk

IL11

-0.1845 Risk

LCK

0.0266 Protection

RND3

-0.2833 Risk

SH3BGR

0.4528 Protection

WNT3A

0.3538 Protection

Table 28: Model No. 7

Gen

Coefficient Property AUROC of the risk score

BAG1

-0,1602 Risk 0.6787

BRCA1

-0.4631 Risk

CDC6

-0.1032 Risk

CDK2AP1

-0,3042 Risk

ERBB3

0.4957 Protection

FUT3

-0.4726 Risk

IL11

-0.1426 Risk

LCK

0.3646 Protection

RND3

-0.4320 Risk

SH3BGR

0.0198 Protection

WNT3A

0.3023 Protection

Table 29: Model No. 8

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.2513 Risk 0.6888

BRCA1

-0.3827 Risk

CDC6

-0.0907 Risk

CDK2AP1

-0.3858 Risk

ERBB3

0.2738 Protection

FUT3

-0.3453 Risk

IL11

-0,1007 Risk

LCK

0.2027 Protection

RND3

-0.0808 Risk

SH3BGR

0.0869 Protection

WNT3A

0.2507 Protection

Table 30: Model No. 9

Gen

Coefficient Property AUROC of the risk score

BAG1

-0.3425 Risk 0.6717

BRCA1

-0.3023 Risk

CDC6

-0.0782 Risk

CDK2AP1

-0.4674 Risk

ERBB3

0.0519 Protection

FUT3

-0.2181 Risk

IL11

-0.0588 Risk

LCK

0.0407 Protection

RND3

-0.2296 Risk

SH3BGR

0.1539 Protection

WNT3A

0.1992 Protection

Table 31: Model No. 10

Gen

Coefficient Property AUROC of the risk score

BAG1 BRCA1 CDC6 CDK2AP1 ERBB3 FUT3 IL11 LCK RND3 SH3BGR WNT3A

-0.4336 -0.2218 -0.0657 -0.0490 0.3301 -0.0909 -0.0169 0.3788 -0.3783 0.2209 0.1477 0,3686239 Risk Risk Risk Protection Risk Risk Protection Risk Risk Protection Protection 0.6797

Claims (6)

1. A method of providing a prognosis for non-small cell lung cancer (NSCLC) in a subject, the method comprising the steps of: 5

(to)

contacting a biological sample of the subject with reagents that specifically bind each member of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; Y

(b)

determine a subject risk score based on the expression nucleic acid levels of the

10 biomarkers in the sample or determine whether biomarkers are differentially expressed or not at the level of nucleic acid in the sample; Y (c) provide a prognosis for NSCLC based on the subject's risk score or differential expression. 15 2. A method of determining a treatment plan for a subject, the method comprising the steps of: (a) contacting a biological sample of the subject with reagents that specifically bind each member of a biomarker panel consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A; 20 (b) determine whether the markers are expressed differentially or not at the level of nucleic acid in the sample;

(C)

 provide a forecast for the NSCLC;

(d)

determine a risk assessment for 5-year mortality based on the prognosis for NSCLC; Y

(and)

devise a treatment plan based on risk assessment.

The method of claim 1, wherein the increased expression of BAG1, BRCA1, CDC6, CDK2AP1, FUT3, IL11 or RND3 contributes to an increased risk score, and wherein the increased expression of ERBB3, LCK, SH3BGR or WNT3A contributes to a decreased risk score, in which an increased risk score indicates an increased risk of long-term mortality of the subject.

The method of any one of claims 1 to 3, wherein the method further comprises normalizing the expression levels of the biomarkers with respect to constitutive genes selected from the group consisting of ESD, TBP, YAP1 and any combination of the same, and, optionally, the levels of expression of the biomarkers are normalized against the average CT value of the constitutive genes.

The method of any one of claims 1 to 4, wherein the reagents are nucleic acids, oligonucleotides or sets of PCR primers.

6. The method of any one of claims 1 to 5, wherein the NSCLC is non-squamous NSCLC.

The method of claim 6, wherein:

(to)

 the non-squamous NSCLC is stage I;

(b)

 non-squamous NSCLC is stage II;

(C)

 the non-squamous NSCLC is stage III; or 45 (d) the non-squamous NSCLC is stage IV.

8. The method of any one of claims 1 to 7, wherein the sample is from a surgically removed tumor, from lung tissue or a lung tumor biopsy.

The method of any one of claims 1 to 8, wherein the prognosis provides a risk assessment and optionally the risk assessment is for a 5-year mortality.

10. The method of claim 9, wherein the forecast provides: 55 a) a high risk assessment for 5-year mortality; b) an intermediate risk assessment for 5-year mortality; or c) a low risk assessment for 5-year mortality. 11. A kit comprising reagents that specifically bind each member of a biomarker panel 60 consisting of BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR and WNT3A, in which the reagents are sets of PCR primers. 12. Use of the kit of claim 11 in the method of any one of claims 1-10.

Gene name Chromosome Reference sequence Protein site Functions and relevant biological routes Role of the algorithm

BAG1

Atanogen associated with BCL2 9p12 NM_004323 Cytoplasm Apoptosis, cell surface receptor linked signaling Forecast

BRCA1

Breast cancer 1, early onset 17q21-q24 NM_007294 Nucleus Induction of apoptosis; protein ubiquitination; regulation of the DNA damage control points of the G2 / N transition; DNA repair regulation Forecast

CDC6

Cell division cycle counterpart 6 17q21.3 NM_001254 Nucleus Regulation of transcription involved in the G1 / S phase of the mitotic cell cycle; regulation of cell proliferation Forecast

CDK2AP1

Protein 1 associated with cyclin-dependent kinase 2 12q24 NM_004642 Nucleus Regulation of the S phase of the mitotic cell cycle; DNA dependent DNA replication Forecast

ERBB3

Homologous 3 of the viral oncogene of erythroblastic leukemia V-erb-b2 12q13 NM_001982 Cytoplasmic membrane Regulation of cell adhesion; transmembrane tyrosine kinase protein receptor signaling, phosphoinositide-3-kinase cascade regulation; regulation of cell proliferation Forecast

FUT3

Fucosyltransferase 3 19q13.3 NM_000149 Cytoplasm Carbohydrate metabolism; glycosylation of protein amino acids Forecast

IL11

Interleukin 11 19q13.3-q13.4 NM_000641 Extracellular Regulation of cell proliferation; B lymphocyte differentiation; megakaryocyte differentiation, phosphorylation regulation of peptidyl serine; MAPKKK waterfall regulation Forecast

LCK

Lymphocyte-specific tyrosine kinase protein 1q34.3 NM_001042771 Cytoplasm Induction of apoptosis; activation of caspase activity; regulation of T cell receptor signaling; leukocyte migration Forecast

RND3

GTPase 3 of the Rho family 2q23.3 NM_005168 Cytoplasm Cell adhesion; Small GTPase-mediated signal transduction; organization of the actin cytoskeleton Forecast

SH3BGR

Protein rich in glutamic acid binding to the SH3 domain 21q22.3 NM_007341 Cytoplasm Assembly of protein complexes Forecast

WNT3A

Wingless type MMTV integration site family, member 3A 1q42 NM_033131 Extracellular Wnt canonical receiver signaling; development of multicellular organisms; regulation of cell proliferation and differentiation; regulation of nuclear translocation of catenin protein Forecast

ESD

Esterase D 13q14.1-q14.2 NM_001984 Cytoplasm Recycling sialic acids; serine hydrolysis Reference

TBP

TATA box binding protein 6q27 NM_003194 Nucleus Start of transcription from the RNA polymerase II promoter; RNA elongation from RNA polymerase II promoter; transcription from RNA polymerase III promoter Reference

YAP1

Protein associated with Yes 1 11q13 NM_006106 Nucleus Hippo signaling waterfall; transcription regulation; contact inhibition Reference

 Figure 4